Sibonix Growth Engine

New Ideated All
Jul 16, 13:55 linkedin_author_expansion seo high 83.2 processed

The AI SEO Plugin is ready. 25 skills + a /bootstrap command to get your brand ranked by ChatGPT, P

https://www.linkedin.com/posts/andrey-soloviev_the-ai-seo-plugin-is-ready-25-skills-activity-7476240668458401792-foCL?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGnjYh8BS9fABVGN_pY_pxAAw51tK1JtvKo

This free AI SEO plugin offers an immediate way to optimize brand visibility across ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews using a bootstrap command. For Sibonix, securing this tool is a priority to analyze its 25 built-in skills and see how we can integrate these LLM-ranking tactics into our own client delivery stack. The concrete next step is to download this free plugin and run the bootstrap command on our own brand to test its effectiveness.

Read full content
The AI SEO Plugin is ready. 25 skills + a /bootstrap command to get your brand ranked by ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews. The same stack we use before, during, and after every client engagement. Yours for FREE ↓ Without it, Claude doesn't know you
Ideas (7)
We are stress-testing the new AI SEO plugin on Sibonix to dissect its 25 skills before integrating them into our client delivery systems.
We just downloaded the new free AI SEO plugin to run the bootstrap command on our own brand.
linkedin_post
LLM optimization is replacing traditional SEO, and we are auditing this new tool to automate how clients rank in Perplexity and Claude.
If Claude and Perplexity do not know your brand exists, your traditional SEO stack is failing.
carousel
We are analyzing the 25 built-in skills of the new AI SEO release to build these LLM-ranking tactics directly into our AI agent systems.
We are auditing all 25 skills in the new AI SEO plugin to see which ones actually rank you in Gemini and ChatGPT.
short_video_script
A specialized '/bootstrap' command within the AI SEO plugin designed to instantly format and prepare brand data for indexing by LLM engines like Claude, ChatGPT, and Perplexity.
We plan to download the plugin, run the '/bootstrap' command on the Sibonix brand domain, and record a screen walkthrough of the step-by-step configuration. We will show the exact markdown and structured schema payload generated, then upload it to a clean Claude Projects knowledge base to verify how it immediately alters Claude's retrieval behavior regarding our brand.
live_workflow_walkthrough
A suite of 25 distinct built-in optimization skills targeting Generative Engine Optimization (GEO) across platforms including Gemini, Claude, Perplexity, and Google AI Overviews.
We will build a side-by-side comparative matrix selecting two key skills from the plugin—specifically the citation-source generator and the LLM-intent alignment skill. We will run these optimizations on a test client page and show a before-and-after comparison of how Perplexity cites the page's content when answering industry-specific queries.
before_after_comparison
A `/bootstrap` command within the AI SEO plugin that automatically initiates brand optimization designed to get a brand recognized and ranked by major engines like ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews.
We will download the free AI SEO plugin, execute the `/bootstrap` command targeting the Sibonix brand domain, and record a walkthrough of the exact optimization assets and markdown files it generates. We will then show how these generated files are structured to feed LLM crawlers.
live_workflow_walkthrough moderate — Requires downloading the plugin, setting up an environment to run the command on our domain, and recording the output files.
The tool's 25 built-in skills designed to solve the problem of LLMs like Claude failing to recognize or recommend your brand.
We will run a series of baseline brand-awareness queries about Sibonix across Claude, Perplexity, and ChatGPT, document the gaps in their knowledge, apply the plugin's recommended schema and markdown optimizations to a test page, and then compare the LLMs' responses after prompting them with the newly optimized context.
before_after_comparison moderate — Requires running baseline queries, deploying the plugin's output to a live test URL, and using LLM reading tools to compare the before and after responses.
Jul 16, 13:55 linkedin_author_expansion ai_marketing high 83.2 processed

Your competitors are currently STEALING your leads... (all because they own LLM visibility) And th

https://www.linkedin.com/posts/andrey-soloviev_your-competitors-are-currently-stealing-your-activity-7472957202601943040-yE4O?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGhj1BoB0uFfUiVqLl94jeduD-07ABoFfnY

Although the source text is truncated, it highlights a critical shift: competitors are stealing leads by dominating LLM visibility while brands remain stuck optimizing only for Google. For Sibonix, the immediate next step is to audit our clients' current visibility within AI search tools and develop a structured optimization framework to ensure they are recommended as top options. We must treat LLM optimization as a core, high-priority service to keep our clients from losing market share to AI-optimized competitors.

Read full content
Your competitors are currently STEALING your leads... (all because they own LLM visibility) And that’s not an exaggeration. While you're still optimizing for Google, AI tools are: - Shaping the entire category narrative without you - Recommending your competitors as the “top
Ideas (2)
Competitors are securing high-intent leads by optimizing their brand footprint to be recommended as top-tier solutions by AI search engines like Perplexity and ChatGPT Search.
We will build an automated LLM Share-of-Voice auditing agent using Python and the Perplexity API to query 50 industry-specific buyer intent prompts. The workflow will parse the responses to generate a comparative visualization showing how often our client is recommended versus their competitors, alongside the exact sources cited by the LLM.
live_workflow_walkthrough moderate — Requires Python scripting, Perplexity API access, and a structured query list, taking roughly 3 to 4 hours to build and run.
AI tools shape the overarching industry category narratives and define market standards without direct input from brands that neglect LLM optimization.
We will run a narrative alignment audit by querying Claude 3.5 Sonnet on emerging category definitions, map the LLM's synthesized narrative against our client's core messaging, and build an optimization plan targeting high-authority index sources (like Reddit and niche industry wikis) to force a narrative correction.
before_after_comparison moderate — Requires querying multiple frontier LLMs, mapping brand messaging docs, and about 3 hours of analysis to draft the narrative injection blueprint.
Jul 16, 13:55 linkedin_author_expansion seo medium 58.2 processed

How the top 1% of SEO founders are doing AI SEO. Workflows, agents, shortcuts, automations, rules:

https://www.linkedin.com/posts/andrey-soloviev_how-the-top-1-of-seo-founders-are-doing-activity-7479840524892401664-RDwi?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGnjYh8BS9fABVGN_pY_pxAAw51tK1JtvKo

While the provided content cuts off after the first point, the premise of moving away from basic ChatGPT prompts toward structured site rulebooks and workflows is the exact right approach. Sibonix should locate the full post to analyze the remaining six tactics. Building these structured rulebooks into our AI agent systems will give our clients a massive edge over competitors using basic prompting.

Read full content
How the top 1% of SEO founders are doing AI SEO. Workflows, agents, shortcuts, automations, rules: Most people paste a URL in ChatGPT, ask "how's my SEO?" and think "I'm AI native". You're not, sorry... The top 1% set up 7 things most founders never touch: 1/ A site rulebook
Ideas (2)
Implementing a structured 'site rulebook' to guide AI content generation rather than relying on generic, single-prompt URL analysis.
We will build a structured JSON-based 'Site Rulebook' defining exact brand voice parameters, keyword mapping, and internal linking constraints for a mock SaaS brand. We will then run two content generation tests—one using Claude 3.5 Sonnet guided by this rulebook, and one using a standard ChatGPT prompt with just a URL—and compare the alignment and SEO-readiness of the output drafts.
before_after_comparison moderate — Requires drafting a structured JSON rulebook schema, running two API-based generation tests, and formatting the output comparison.
Moving from manual prompt-and-paste SEO audits to automated multi-agent workflows that run programmatic site checks.
We will build an automated workflow in Make.com that triggers when a new URL is published, crawls the page using an Apify scraping agent, evaluates the HTML against a target keyword list using GPT-4o, and automatically pushes structured optimization tickets directly to a Slack channel. We will record a walkthrough of this live agent pipeline executing end-to-end.
live_workflow_walkthrough substantial — Requires configuring a multi-step Make.com scenario, integrating Apify, setting up OpenAI API assistants, and building the Slack notification payload.
Jul 16, 13:54 linkedin_author_expansion ai_marketing high 83.2 processed

I tested every AI tool a solo founder/marketer might need. Here's the $60/month stack that actually

https://www.linkedin.com/posts/rananjayraj_aiformarketers-solofounder-claudeai-activity-7474823466362294272-z4-U?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGhtqnkBlAH9R4sBJ5YIgAmHDEXmcztJvdk

This lean $60/month stack demonstrates that expensive subscriptions like Zapier, Perplexity, and OpenClaw are redundant when self-hosted n8n and Hermes Agent are utilized. Sibonix should immediately evaluate Hermes Agent as a secure, container-hardened alternative for self-hosted agentic workflows to avoid the security vulnerabilities of older tools. Additionally, we should audit our automation costs to ensure we are leveraging self-hosted n8n for unlimited free executions.

Read full content
I tested every AI tool a solo founder/marketer might need. Here's the $60/month stack that actually ships work. No sponsorships. No "10 tools you NEED" listicle. Just what earns a seat on my card, what I run for free, and what I dropped after testing it properly. The 3 I pay for ($60/month total, nothing more): → Claude Pro, $20/month: writing, Cowork automation, every Skill I've built, and Claude Code for scripts and quick fixes, all in one plan. → ChatGPT Plus, $20/month: Codex acts as a second-pair-of-eyes code reviewer on anything Claude Code ships, plus native image generation for ad creative and quick social graphics. → Google AI Pro, $20/month: Gemini lives inside Docs, Sheets, and Gmail. I use it for multimodal competitor breakdowns (video and image together) and campaign briefs I don't want to leave Workspace for. The 6 I run free and haven't needed to upgrade: → Grok: free tier is capped at roughly 10 prompts every 2 hours, enough for quick X trend checks. → NotebookLM: free plan gives 100 notebooks and 3 audio overviews a day. Source-grounded research, no hallucination risk on facts I'd otherwise have to double-check. → Notion: the free plan. Full AI got moved behind the $20 Business tier this year, but I don't need it there, Claude already does that work. → Obsidian: fully free, no gated features. My swipe file and evergreen notes live here. → Hermes Agent: free, open-source, MIT licensed. Same self-hosted agent idea as OpenClaw, built with container hardening from day one. → n8n: self-hosted, free, unlimited executions. The plumbing connecting everything above. The ones I dropped after testing: → Perplexity: a 2026 audit found a 37% error rate on its answers. Claude, ChatGPT, and Gemini already cover research between them, paying $20/month for a fourth research tool stopped making sense. → OpenClaw: genuinely interesting open-source agent, but it wants shell and file access across your machine, it's 6 months old now, and it's already got a logged CVE rated 8.8 severity. Hermes Agent covers the same ground with container hardening built in, so that's the one in my free tier instead. → Lovable/Replit: solid for building actual apps, but Claude Cowork already covers the agentic work I need. Paying twice for overlapping capability didn't make sense. → Zapier and Make: both good products, both recurring costs for something n8n already does for free once it's set up. Full breakdown (cost, free-tier ceiling, what each tool actually owns) is in the image. Building this kind of stack and want help mapping it to your own workflows? That's the work I do. What's in your stack that I missed? #AIforMarketers #SoloFounder #ClaudeAI #MarketingAutomation #AIStack
Ideas (2)
Using self-hosted n8n to achieve unlimited free automation executions, bypassing the scaling costs of subscription services like Zapier or Make.
We will deploy n8n on a self-hosted cloud instance and build a marketing automation workflow that ingests social feeds, analyzes them with Claude's API, and updates Google Sheets. We will document the setup process and present a cost-benefit calculation comparing n8n's flat hosting fee against Zapier's pricing tier for 50,000 monthly tasks.
live_workflow_walkthrough moderate — Requires spinning up a cloud VPS, installing n8n via Docker, configuring API credentials, and building the multi-step test workflow.
Utilizing Hermes Agent as a secure, container-hardened, open-source agentic tool to safely execute local code and file operations without exposing the host system to CVE vulnerabilities.
We will draft a deployment plan to install Hermes Agent in a sandboxed Docker container, run a file-processing agent task, and explicitly show how the container configuration blocks unauthorized shell commands from escaping to the host machine. This will demonstrate secure local execution of AI-generated scripts.
screen_recording substantial — Requires installing Hermes Agent, configuring Docker security policies, writing a test agent command, and recording the execution and containment behavior.
Jul 16, 13:54 linkedin_author_expansion ai_marketing high 83.2 processed

Claude Sonnet 5 shipped this week. It's now the default model across Claude.ai, Claude Code, and Cow

https://www.linkedin.com/posts/rananjayraj_claude-sonnet-5-shipped-this-week-its-now-activity-7478456555877740544-ShmH?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGhtqnkBlAH9R4sBJ5YIgAmHDEXmcztJvdk

Claude Sonnet 5 is a massive upgrade for our AI agent builds, offering native agentic planning and a game-changing 1M-token context window. We must immediately leverage the discounted $2/$10 pricing through August 31 to automate manual workflows like competitor audits and campaign QA. The concrete next step is to migrate our marketing workflows to Sonnet 5 to process entire brand guidelines and campaign histories in single sessions.

Read full content
Claude Sonnet 5 shipped this week. It's now the default model across Claude.ai, Claude Code, and Cowork. Three things matter here if you're running any kind of AI automation in your business: → It's built to be agentic. Planning, tool use, browsers, terminals, running autonomously at a level that used to need a bigger, pricier model. That's the tier your marketing workflows probably live in right now. → Native 1M-token context window. That's not a spec bump, it's a different way of working. Full campaign history, brand guidelines, and competitor research in one session instead of chunking everything into pieces and hoping Claude remembers slide 3. → Pricing dropped to $2/$10 per million tokens through August 31. If cost was the reason you kept a workflow manual, that math changed this week. I've been testing what this unlocks for a few automation workflows I run: reporting, competitor audits, campaign QA. The context window is the part that's actually changing how I set things up, not the speed.
Ideas (8)
Claude Sonnet 5's 1M-token context window eliminates the need for complex database integrations to keep AI agents aligned with brand guidelines.
Stop building complex retrieval systems just to keep your AI agents on-brand.
linkedin_post
The temporary pricing drop through August 31 makes running high-volume competitor audits mathematically negligible in cost.
Running daily competitor audits just became 80 percent cheaper.
carousel
Sonnet 5's native agentic planning allows us to deploy marketing systems that execute multi-step campaign QA without human intervention.
We are migrating our client QA workflows to Sonnet 5 to eliminate manual review bottlenecks.
short_video_script
Claude Sonnet 5 features a native 1M-token context window that can process entire brand guidelines, campaign histories, and competitor research in a single session without chunking.
We will build a workflow that feeds a brand's 500-page historical asset archive and complete style guide into a single Sonnet 5 session. We will show a walkthrough of the model auditing a new multi-channel campaign draft against this massive history to flag brand voice violations.
live_workflow_walkthrough
Claude Sonnet 5 includes native agentic planning, tool use, browser navigation, and terminal control to run workflows autonomously.
We will build an autonomous competitor auditing agent powered by Sonnet 5's tool-use capabilities. We will capture a screen recording of the agent dynamically planning its steps, opening a browser to extract pricing data from live competitor sites, and compiling a structured CSV analysis locally.
screen_recording
Native 1M-token context window that enables processing entire brand guidelines, competitor research, and full campaign histories in a single session without chunking.
We will build a workflow that ingests a massive 300-page brand repository (containing PDF brand guidelines, historical ad performance sheets, and competitor audits) into Claude Sonnet 5 and run a prompt to generate a new, fully aligned Q3 ad campaign strategy, showcasing how the model synthesizes all inputs without loss of context.
live_workflow_walkthrough moderate — Requires gathering ~300 pages of real brand/campaign documents, configuring the API connection, and recording the prompt execution and output quality.
Native agentic planning and tool use capabilities that allow autonomous execution of complex, multi-step marketing tasks like competitor audits.
We will build an autonomous research agent using Sonnet 5's tool-use API that takes a target competitor's homepage URL, plans its own navigation path, executes web-scraping calls to extract pricing and messaging, and compiles a structured competitive analysis report.
screen_recording substantial — Requires writing a Python script integrating Claude's tool-use API with a scraping/browser tool, debugging the agent's planning loops, and capturing the execution video.
Discounted $2 input / $10 output per million tokens pricing (through August 31), which shifts the economics of running high-volume manual QA workflows.
We will build a batch-processing script that runs automated brand compliance QA on 100 mock display ads, tracking the exact token usage and API costs under Sonnet 5's promotional pricing, and present a cost-benefit spreadsheet comparing it to manual labor rates.
before_after_comparison quick — Requires writing a simple API batch script, generating mock ad text, and building a cost-comparison sheet based on the $2/$10 pricing.
Jul 16, 13:54 linkedin_author_expansion organic_social high 83.2 processed

LinkedIn started quietly cutting reach for AI comments in May. No warning, no notification. The acco

https://www.linkedin.com/posts/rananjayraj_linkedin-started-quietly-cutting-reach-for-activity-7481331403171942400-es69?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGhsAoYBjYCTB5mL2W-F96Uuxpw_PDaXBxE

Sibonix must immediately audit all LinkedIn workflows and halt any automated AI commenting, DM replies, or connection notes to protect account reach from LinkedIn's quiet distribution cuts. We should restrict AI tools like Claude strictly to backend tasks like drafting from voice notes, researching hooks, and planning layouts. Moving forward, we must apply the author's exact test: if a user would feel differently knowing an interaction was automated, it must be done manually.

Read full content
LinkedIn started quietly cutting reach for AI comments in May. No warning, no notification. The account just stops traveling. I automate about half my LinkedIn work with Claude. Here is the exact line I never cross, and the one test that decides it. What I let Claude draft: • First drafts of posts, from my own voice notes and analytics. I rewrite every one. • Carousel and infographic layouts • Research on what's working: hooks, formats, timing patterns • Summaries of my week's comments and DMs so nothing falls through What I never automate: • Comments on other people's posts. If I didn't read your post, I have no business commenting on it. • DM replies. People can tell. And the moment they can tell, you've lost them. • Connection request notes. A templated "I love your content!" does more damage than no note at all. • Anything posted without me reading it end to end first The May crackdown made this a reach problem, not just a taste problem. Automated comments and generic AI replies now get flagged, and the account posting them loses distribution quietly. But honestly, the algorithm just caught up to what readers already knew. A comment that could have been written by anyone, about any post, was never doing anything for you. The test I use before automating anything: would the other person feel differently about this interaction if they knew a machine did it? If yes, I do it myself. Where do you draw the line? I suspect mine is more conservative than most, and I'd genuinely like to hear the counterargument.
Ideas (7)
We are pausing all automated frontend interactions on LinkedIn and shifting our AI systems strictly to backend research and drafting to protect client reach.
LinkedIn is quietly killing the reach of accounts using AI for comments and DMs.
linkedin_post
Our agency now applies a single rule for LinkedIn operations: if a prospect would feel cheated knowing an interaction was automated, we force it to be manual.
If your prospects knew an AI wrote your DM reply, would they still buy from you?
short_video_script
We rebuilt our LinkedIn system to ban automated engagement, redirecting Claude entirely to backend tasks like layout planning and voice-note transcription.
We just stripped all automated commenting and messaging out of our LinkedIn systems.
carousel
LinkedIn quietly cuts account reach and distribution when detecting automated AI comments, DM replies, and templated connection notes.
We will build a Make.com scenario that replaces fully automated LinkedIn outbound sequences with an approval-gated system. The workflow will pull incoming DMs via Phantombuster, feed them to Claude to draft a context-aware response, and send a Slack notification with 'Approve' and 'Edit' buttons so a human must manually copy and send the final message, keeping the account completely safe from algorithm flags. We will record a live walkthrough of this setup in action.
live_workflow_walkthrough
Using AI strictly for safe backend tasks, such as generating authentic first drafts of LinkedIn posts directly from a creator's raw voice notes.
We will build an automation that takes a raw, unstructured audio voice note, transcribes it using the Whisper API, and passes it to Claude 3.5 Sonnet. We will show a screen recording of the exact system prompt used to extract high-impact hooks and format the transcription into a polished, human-sounding LinkedIn draft and carousel layout without losing the creator's unique voice.
screen_recording
Utilizing AI strictly for backend drafting tasks—such as converting raw voice notes into structured LinkedIn post drafts and planning carousel layouts—to keep front-facing interactions entirely human.
We will build a Make.com scenario that takes raw mobile voice memos from a Google Drive folder, transcribes them using OpenAI's Whisper, and routes the transcript to Claude 3.5 Sonnet to output three distinct hook options and a slide-by-slide carousel layout. We will show a screen recording of the exact prompts used to preserve the speaker's natural voice and the final manual edits made to the draft.
live_workflow_walkthrough moderate — Requires configuring a Make.com scenario linking Google Drive, OpenAI Whisper, and Anthropic Claude APIs, taking about 2 hours to test and refine the translation prompts.
Halting automated front-facing LinkedIn activities (AI comments, DMs, and connection notes) to protect account distribution from quiet reach cuts by routing these tasks to manual human queues.
We will build a hybrid prospecting pipeline in Clay where outbound leads are researched and personalized hooks are drafted by AI, but instead of auto-sending them via LinkedIn automation tools, the workflow pushes these drafts into a manual review queue in Slack. We will show a before-and-after comparison of a fully automated sequence setup versus this human-in-the-loop validation pipeline.
before_after_comparison moderate — Requires setting up a Clay table with AI enrichment and configuring a webhook to push manual action items to a Slack channel, taking roughly 2 hours.
Jul 16, 13:54 linkedin_author_expansion ai_marketing high 83.2 processed

𝐁𝐢𝐠 𝐀𝐧𝐧𝐨𝐮𝐧𝐜𝐞𝐦𝐞𝐧𝐭 🎉 Launching BuildWire.AI. For 15+ years I ran B2B marketing at E

https://www.linkedin.com/posts/rananjayraj_%F0%9D%90%81%F0%9D%90%A2%F0%9D%90%A0-%F0%9D%90%80%F0%9D%90%A7%F0%9D%90%A7%F0%9D%90%A8%F0%9D%90%AE%F0%9D%90%A7%F0%9D%90%9C%F0%9D%90%9E%F0%9D%90%A6%F0%9D%90%9E%F0%9D%90%A7%F0%9D%90%AD-launching-activity-7480620827802193920-7C4Z?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGhsAoYBjYCTB5mL2W-F96Uuxpw_PDaXBxE

BuildWire.AI is launching to solve the exact gap Sibonix targets: bridging the divide between marketing strategy and technical AI automation using tools like n8n and Claude. Their three-pronged focus on autonomous marketing workflows, AI license adoption, and optimization for AI search engines (ChatGPT/Perplexity) directly validates our agency's core thesis. We must analyze this positioning to ensure our own AI agent systems and done-for-you marketing services remain highly competitive.

Read full content
𝐁𝐢𝐠 𝐀𝐧𝐧𝐨𝐮𝐧𝐜𝐞𝐦𝐞𝐧𝐭 🎉 Launching BuildWire.AI. For 15+ years I ran B2B marketing at EY, Michelin, and Hitachi Vantara. In April I left to build with AI full time. Since then, a version of the same conversation keeps happening. A founder tells me they know AI could save their team 10+ hours a week. They've already bought the tools. Then comes the quiet admission: nothing is actually wired up. The automation people they talked to didn't understand marketing, and the marketing people didn't understand automation. That gap is exactly where I've been living. Building full time has looked like this so far: → Selected into Hyperagent's Founding 500 → A newsletter with a growing subscriber base → A growing audience across LinkedIn, X, YouTube, and Instagram → An AI learning app, live on the App Store And underneath all of it: my newsletter, research, and content pipeline run on n8n, Hyperagent and Claude Agents I built myself. Some of them have been running since before I quit my job. That's the same kind of system BuildWire now builds for marketing and growth teams. The work is mostly three things: 1. Marketing automations that keep running without you babysitting them 2. Getting your team actually using the AI licenses you're already paying for 3. Making your brand show up when buyers ask ChatGPT or Perplexity instead of Google I'd rather start with honest conversations than a polished sales page. If you've been putting off setting any of this up, or you know someone who has, my DMs are open. No pitch on the first call. And if you're not sure where to start: name the most repetitive thing your team did this week. That's usually the first thing worth automating.
Ideas (7)
Most brands fail at AI adoption because they try to force their existing marketing team to become technical automation engineers.
Buying Copilot or ChatGPT licenses for your marketing team will not automate your marketing.
linkedin_post
The real bottleneck in modern marketing is the gap between the people who write strategy and the machines that execute it.
Marketing agencies that do not build their own proprietary AI agent systems are already obsolete.
carousel
Optimizing for Perplexity and ChatGPT requires a continuous, high-volume content loop that only autonomous AI agents can maintain.
Your brand is invisible on Perplexity because your content team is still writing for Google 2022.
short_video_script
Autonomous marketing automations built on n8n and Claude that manage content pipelines without requiring constant human monitoring.
We will build a live n8n workflow that monitors a Google Drive folder for raw content briefs, routes them to a Claude 3.5 Sonnet agent to generate structured social media copy, and automatically stages them in Buffer, showcasing the exact error-handling and routing logic that keeps the system running autonomously.
live_workflow_walkthrough
Optimizing brand visibility on conversational search engines like ChatGPT and Perplexity to ensure a brand is recommended during buyer research.
We will build a comparative audit showing how a brand is currently cited in a Perplexity query for 'best B2B marketing automation tools' versus its visibility after we implement structured entity data and comparison landing pages, mapping the exact citation sources Perplexity crawls to generate its answers.
before_after_comparison
Autonomous marketing and content research pipelines built on n8n and Claude that run continuously without manual babysitting.
Build an active n8n workflow that monitors industry RSS feeds, uses a Claude API agent to filter for trending topics, automatically drafts a newsletter section in a Google Doc, and pings a Slack channel for final approval. We will record a live workflow walkthrough of the canvas running in real-time to show how the logic steps execute.
live_workflow_walkthrough moderate — Requires setting up an n8n cloud account, configuring Claude API credentials, and mapping the data nodes to Google Docs and Slack (approx. 2-3 hours).
Optimizing a brand's digital footprint so it is actively cited and recommended when buyers search on conversational AI engines like ChatGPT and Perplexity.
Create a before-and-after comparison showing how a brand's visibility changes in Perplexity. We will query Perplexity on a specific B2B software category, document the initial citations, implement a structured schema and digital PR update on a test site, and then record the updated Perplexity query showing our brand now appearing in the cited sources.
before_after_comparison substantial — Requires deploying optimized site schema, indexing the test pages, waiting for LLM crawler cycles, and running comparative queries over 1-2 weeks.
Jul 16, 13:54 linkedin_author_expansion ai_marketing high 83.2 processed

OpenAI shipped 3 major releases in 48 hours. Most feeds are covering them separately. They only mak

https://www.linkedin.com/posts/rananjayraj_open-ai-updates-activity-7481221137406930944-w14w?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGhsAoYBjYCTB5mL2W-F96Uuxpw_PDaXBxE

OpenAI's release of GPT-5.6 with parallel agent execution and ChatGPT Work directly impacts how we build agent systems. Sibonix must immediately test these new models and the 'ultra' setting to verify OpenAI's cost-per-outcome claims before migrating any client workflows. The battle has shifted to agent UX, and we need to assess if ChatGPT Work's document and slide generation can replace our current tool stack.

Read full content
OpenAI shipped 3 major releases in 48 hours. Most feeds are covering them separately. They only make sense read together. 𝐉𝐮𝐥𝐲 𝟖: 𝐆𝐏𝐓-𝐋𝐢𝐯𝐞. A new generation of voice models built on full-duplex architecture. It listens and speaks at the same time, waits when you pause, and hands hard questions to a frontier model behind the scenes while the conversation keeps flowing. 𝐉𝐮𝐥𝐲 𝟗: 𝐆𝐏𝐓-𝟓.𝟔. The new family (Sol, Terra, Luna) hits general availability. Pricing per 1M tokens: Sol $5 in / $30 out, Terra $2.50 / $15, Luna $1 / $6. Plus an 'ultra' setting that runs four agents in parallel on one task. 𝐉𝐮𝐥𝐲 𝟗: 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐖𝐨𝐫𝐤. An agent mode that pulls context from your tools and files, shows you the plan, then delivers finished docs, sheets and slides. Desktop today for all plans. The way I read it: one launch per layer. Model (5.6). Interface (Live). Workflow (Work). Three things stood out to me: 1. The pricing is the headline. OpenAI is competing on cost per outcome now, not benchmark bragging rights. Worth noting the benchmark numbers are OpenAI's own reported figures, so test before you migrate anything. 2. Voice just became a plausible work interface. Full-duplex is the difference between a demo and something you could actually run a briefing with. 3. ChatGPT Work is a direct answer to Claude Cowork. The agent race has moved from models to UX. Full breakdown in the carousel. What's the first workflow you'd hand over to an agent mode like this?
Ideas (9)
We are stress-testing OpenAI's new 'ultra' parallel agent setting to verify their cost-per-outcome claims before migrating any client marketing workflows.
OpenAI claims its new parallel agent setting cuts cost per outcome, but we are not migrating client workflows until we run the stress tests ourselves.
linkedin_post
We are auditing ChatGPT Work's document and slide generation to see if it can genuinely replace our specialized agency tool stack.
ChatGPT Work wants to replace your marketing tool stack.
carousel
The AI bottleneck is no longer the underlying model strength but how fluidly the interface handles multi-agent execution.
Stop looking at GPT-5.6 benchmark scores.
short_video_script
GPT-5.6's 'ultra' setting that runs four agents in parallel on one task to optimize cost-per-outcome.
We will build a benchmark test using the GPT-5.6 API to run four parallel agents (configured as a researcher, strategist, copywriter, and editor) on a standardized ad campaign creation task. The plan is to measure the exact token usage and execution time, comparing this parallel 'ultra' run's cost and quality directly against a traditional sequential GPT-4o chain.
before_after_comparison
ChatGPT Work's agent mode that pulls context from local tools and files to generate finished documents, sheets, and slides.
We plan to connect ChatGPT Work to a Google Drive folder containing raw product spec sheets and brand guidelines for a mock client. We will record a video showing the agent analyzing the files, displaying its step-by-step layout plan, and generating a formatted 5-slide Google Slides presentation in real time.
screen_recording
GPT-Live's full-duplex voice architecture that listens and speaks simultaneously while handing off complex questions to a background frontier model.
We will build a voice-driven marketing briefing assistant using the GPT-Live API. The live walkthrough will show a user interrupting the assistant mid-sentence with a complex campaign ROI calculation, demonstrating how the voice pauses instantly, processes the math via the background model, and delivers the answer without breaking conversational flow.
live_workflow_walkthrough
GPT-5.6's 'ultra' setting which runs four parallel agents on a single task to optimize cost-per-outcome.
We will build a parallel marketing research workflow using the GPT-5.6 Luna model under the 'ultra' setting to analyze competitor ad strategies, running four agents simultaneously to compile a unified report, and compare its actual token cost and execution speed against our standard sequential GPT-4o agent workflow.
before_after_comparison substantial — Requires API access to the GPT-5.6 family, writing a parallel execution script, and setting up Helicone to track exact token spend.
ChatGPT Work's agent mode that connects to local files and tools to output complete docs, sheets, and slides based on a visible plan.
We will set up a live walkthrough where we feed ChatGPT Work a raw client brief and a campaign performance CSV, let it generate its multi-step plan, and record it outputting a formatted Google Slides presentation and budget sheet.
live_workflow_walkthrough moderate — Requires desktop access to ChatGPT Work, preparing a mock client dataset, and recording the end-to-end generation process.
GPT-Live's full-duplex voice architecture that listens and speaks simultaneously and hands off complex queries to a background frontier model.
We will record a screen and audio session testing GPT-Live as a real-time campaign briefing assistant, where we intentionally interrupt its speech with complex marketing math to show how it pauses, processes the handoff, and resumes.
screen_recording quick — Requires mobile or desktop access to the GPT-Live voice interface and a pre-planned script of complex interruptions.
Jul 16, 13:54 linkedin_author_expansion ai_marketing high 83.2 processed

Your AI writing sounds like everyone else's AI writing. Not because your prompts are bad. Because t

https://www.linkedin.com/posts/rananjayraj_your-ai-writing-sounds-like-everyone-elses-activity-7482450749168476160-3Hme?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGhsAoYBjYCTB5mL2W-F96Uuxpw_PDaXBxE

This 'brand voice card' is a highly practical asset for Sibonix to standardize client voices across our AI agent systems. Instead of relying on complex, fluctuating prompts, we should immediately build and implement this 8-line template for every brand we manage. This ensures consistent, non-generic output across ChatGPT, Claude, and any new tools we deploy.

Read full content
Your AI writing sounds like everyone else's AI writing. Not because your prompts are bad. Because the tool has no idea who you are, so it writes toward the average of everything it has read. I fixed this with a one-page "brand voice card". Eight lines: what I do, who I talk to, my tone, the words I use, the words I ban, my formatting rules, and two real samples of my writing. Now you paste it into any tool before you ask for anything. ChatGPT, Claude, Grok, the new stuff that launches next month. Same voice, every time. A prompt is a one-time instruction. A voice card is an asset you build once and reuse forever. I wrote the full breakdown as an article: the exact 8-line template, a filled example you can copy, the 3 ways to deploy it, and the one line that turns it into a final-pass quality check. Full article linked in the comments. What three words describe your voice? Drop them below and I'll tell you the one word I'd ban from your list.
Ideas (7)
We embed 8-line brand voice cards directly into the system prompt level of our AI agent systems to automate brand consistency without manual copying and pasting.
Stop pasting your brand voice guidelines into ChatGPT every time you need a post.
linkedin_post
Complex prompt engineering for brand voice is obsolete because a standardized 8-line asset keeps multi-agent systems aligned across different LLMs.
Your 1,000-word AI prompt is actually making your brand voice worse.
carousel
We run client campaigns across Claude, GPT-4, and Llama simultaneously by feeding them the exact same 8-line voice card as a system constant.
Claude and ChatGPT will never write the same way unless you give them the same 8-line guardrail.
short_video_script
Using a standardized, reusable 8-line 'brand voice card' (defining audience, tone, formatting, banned words, and samples) as a system prompt prefix to eliminate generic, average AI writing styles.
We plan to build a standardized 8-line Brand Voice Card in a Google Sheet for a B2B marketing client. We will then show a side-by-side comparison of a blog introduction generated by GPT-4o using a standard prompt versus one generated with the brand voice card prepended, highlighting how the card successfully filters out generic AI vocabulary and enforces strict formatting constraints.
before_after_comparison
Deploying a single, static brand voice asset across different LLMs (such as ChatGPT and Claude) to achieve identical stylistic and tonal outputs across platforms.
We will build a Make.com automation that pulls a JSON-formatted brand voice card from Airtable and feeds it as a system instruction to both Anthropic's Claude 3.5 Sonnet and OpenAI's GPT-4o. We will record a walkthrough of the workflow showing that both models produce cohesive, brand-aligned LinkedIn posts that respect the exact same constraints despite their different native architectures.
live_workflow_walkthrough
An 8-line brand voice card template that standardizes output style and tone consistently across multiple distinct LLMs instead of relying on complex, fluctuating prompt instructions.
We will draft a standard 8-line brand voice card for a sample B2B brand, then run a standard newsletter prompt with and without the card across Claude 3.5 Sonnet, GPT-4o, and Gemini 1.5 Pro. We will present a side-by-side comparison of the outputs to visually demonstrate how the card eliminates generic AI-isms and maintains consistency across all three models.
before_after_comparison moderate — Requires drafting a sample 8-line voice card, running 6 prompt iterations across three LLM interfaces, and compiling the comparative text outputs into a side-by-side visual layout.
Using the brand voice card as an automated, final-pass quality check to audit, score, and correct draft AI outputs.
We will build a Make.com automation workflow that takes raw, drafted marketing copy, passes it to a secondary LLM step programmed with the brand voice card as an evaluator, and record a walkthrough of the system automatically flagging banned words and reformatting violations in real-time.
live_workflow_walkthrough substantial — Requires building a multi-step workflow in Make.com, connecting OpenAI or Anthropic APIs, creating a draft with intentional voice violations, and recording the execution of the automated audit.
Jul 16, 13:54 linkedin_author_expansion ai_marketing high 83.2 processed

Most marketers pick one AI model and run everything through it. That's quietly become the most expe

https://www.linkedin.com/posts/rananjayraj_aitools-artificialintelligence-aiproductivity-activity-7475903193130893313-J05P?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGhtqnkBlAH9R4sBJ5YIgAmHDEXmcztJvdk

Relying on a single default AI model for all marketing tasks is a lazy habit that severely degrades output quality. Sibonix must audit its AI agent systems to ensure tasks are routed to specialized models—like Claude for long-form voice, Gemini for data analysis, and lighter models for high-volume routine work. Matching the specific job to the right model is the only way to maintain high-quality client deliverables.

Read full content
Most marketers pick one AI model and run everything through it. That's quietly become the most expensive habit in their AI stack right now. Not expensive in dollars. Expensive in output. You push a 40-page report through a fast creative model, or write subject lines with a heavy reasoning model, then blame "AI" when the result falls flat. The tool wasn't wrong. The match was. I've been testing the current lineup across real work, and the gap between models is wide enough now that the job should pick the model, not your default habit. Here's the map I keep next to me (June 2026): → Long-form writing & brand voice: Claude Opus 4.8. Holds a consistent voice across long documents. → Creative copy & general writing: GPT-5.5. Fast, strong on short-form and idea variety. → Reasoning & data analysis: Gemini 3.1 Pro. Leads on data, logic, and multimodal inputs like PDFs and decks. → Real-time research & trends: Grok 4.3 or Perplexity. Live web, current news, fast lookups. → Image generation: GPT Image 2 or Nano Banana 2. Creative visuals and on-brand graphics. → High-volume routine work: Claude Haiku 4.5 or GPT-5.4-mini. Cheap and fast for subject lines, product descriptions, and bulk drafts. No single model wins everything. The job picks the model now, not the other way around. Which job are you still running through the wrong model? Like, Save & Reshare ♻️ with someone who is still using one model for everything. #AITools #ArtificialIntelligence #AIProductivity #Claude #FutureOfWork
Ideas (8)
Building automated agent systems that route tasks to specialized models eliminates the human bottleneck of manually choosing the right tool.
Most marketing teams waste hours manually switching between different AI models.
linkedin_post
A single-model AI stack produces generic marketing assets that fail to convert.
The default AI model habit is quietly ruining your brand voice.
carousel
Sibonix audits and builds multi-model systems to ensure client deliverables leverage the distinct strengths of Claude, Gemini, and GPT.
If your marketing agency uses one AI model for everything, you are overpaying for average work.
short_video_script
Routing routing-specific marketing tasks to specialized models (such as Claude for long-form brand voice and Gemini for data analysis) yields significantly higher quality than relying on a single default model.
We will build a multi-agent workflow in n8n that splits a marketing campaign brief, routing the competitor data analysis to Gemini 3.1 Pro and the long-form copy generation to Claude Opus 4.8. We will then show a side-by-side comparison of this orchestrator's output against the output of a single default model running the entire prompt.
before_after_comparison
Using lighter, faster models like Claude Haiku or GPT-mini for high-volume routine work (such as bulk subject lines and product descriptions) maintains acceptable quality while optimizing speed and API costs.
We will build an automated bulk email variant generator using Claude Haiku 4.5 to process 100 product SKUs simultaneously. We will record a live walkthrough of the run, showing the real-time generation speed, the final copy quality, and the cost breakdown dashboard.
live_workflow_walkthrough
Matching specific marketing tasks to specialized models (such as Claude for long-form brand voice) produces significantly better outputs than relying on a single default model.
We will build an automated workflow in Make.com that takes a raw brand brief and routes it to Claude for long-form drafting. We plan to present a side-by-side comparison showing the resulting voice consistency and depth against the same brief run through a default generalist model.
before_after_comparison moderate — Requires setting up a Make.com scenario, API keys for both models, and a standard brand voice prompt template to run the test.
Gemini models excel specifically at complex data analysis and logical reasoning over multimodal inputs like PDFs and decks.
We plan to build a script that feeds a complex multi-page PDF marketing report into Gemini and record a screen walkthrough demonstrating its data extraction accuracy and analytical reasoning compared to a standard creative model's output on the same file.
screen_recording quick — Requires a sample multi-page PDF report, access to Gemini and alternative model APIs, and a basic comparison prompt.
Routing high-volume, routine tasks like bulk copywriting to lightweight, cheaper models maintains required quality while drastically reducing operational costs.
We will build a batch processing script that takes a CSV of 200 product SKUs to generate descriptions. We plan to show a live dashboard walkthrough tracking the processing speed, total API cost, and output quality of a lightweight model versus a heavy frontier model.
live_workflow_walkthrough moderate — Requires writing a batch processing script, setting up a simple logging dashboard to track cost metrics, and preparing a sample CSV.
Jul 16, 13:54 linkedin_author_expansion ai_marketing high 83.2 processed

Your ad account is leaking money in places you never check. I built 15 free Claude Skills that find

https://www.linkedin.com/posts/rananjayraj_your-ad-account-is-leaking-money-in-places-activity-7476257968431497216-1Y8g?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGhtqnkBlAH9R4sBJ5YIgAmHDEXmcztJvdk

This is a must-acquire resource for Sibonix since it offers 15 pre-built Claude skills specifically designed to automate paid ad tasks across Google, Meta, and LinkedIn. The concrete next step is to like, comment 'Ads', and connect with the author to get the free prompts and setup docs. We can directly analyze these workflows to improve our own AI agent systems and client delivery.

Read full content
Your ad account is leaking money in places you never check. I built 15 free Claude Skills that find it. Running paid ads across Google, Meta, and LinkedIn used to eat 15+ hours a week. Across 8 client accounts. Manual audits. Mining negatives. Picking apart creative. Building audiences from scratch. Writing copy that won't trip a policy review. So I built the Paid Advertising Agent. 15 skills that talk to each other and work as one system. 𝐆𝐨𝐨𝐠𝐥𝐞 𝐀𝐝𝐬: 🔍 Performance Max Auditor → grades assets, recommends fixes ✍️ Copy Generator → compliant headlines, extensions, policy checks 📊 Search Terms Analyzer → winners, losers, patterns ⛏️ Negative Keyword Miner → finds wasted spend 📈 Performance Diagnostic → health check plus bid optimization 𝐌𝐞𝐭𝐚 𝐀𝐝𝐬: 🎯 Andromeda Auditor → catches Advantage+ mistakes 🧠 Audience Builder → targeting from customer data 📝 Creative Brief Generator → designer briefs from objectives 🔬 Creative Analyzer → scores your ads, explains what's working on Meta 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 𝐀𝐝𝐬: 🎯 Audience Builder → job title and company targeting ✍️ Copy Generator → B2B tone plus briefs 🔬 Creative Analyzer → scores your ads, explains what's working specific to LinkedIn 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴: 📋 Media Planner → budget split, channel mix, KPI forecasts 🎯 Creative Testing Framework → A/B matrices with statistical rigor 📊 Insights Reporter → turns test data into design principles For agencies on 5+ accounts, in-house multi-platform teams, consultants, and solo founders spending $2K+/month. All 15 skills are free. Want the complete Paid Advertising Agent? 1️⃣ Like this post 2️⃣ Comment "Ads" 3️⃣ Connect with me 🤝 I'll send all 15 skills, example prompts, setup docs, and a guide with examples. Follow for more Claude Skills and AI automation workflows 🙂
Ideas (3)
A modular Claude-based agent system that coordinates specialized ad-platform 'skills' (such as Google Performance Max auditing and Meta Andromeda auditing) to analyze performance and generate assets.
We will build a prototype workspace in Claude Projects containing three core agent modules: a Google Search Terms Analyzer, a Meta Andromeda Auditor, and a B2B LinkedIn Copy Generator. We will feed it raw sample ad performance data and record a walkthrough showing how these distinct prompts interact to produce a cohesive cross-channel optimization report.
live_workflow_walkthrough moderate — Requires setting up a Claude Project, drafting three specialized system prompts based on the shared framework, and preparing mock ad performance data (takes ~3 hours).
An automated Google Ads negative keyword miner and search terms analyzer designed to identify wasted spend and recommend bid optimizations.
We will build a Make.com automation that connects a Google Sheet containing raw search term data to the Anthropic API. We will run an anonymized search term report through this flow to showcase a before-and-after comparison of the chaotic raw search queries versus the clean, categorized negative keyword list generated by the AI.
before_after_comparison moderate — Requires building a Make.com scenario, setting up the Anthropic API connection, and structuring a template Google Sheet for the input/output data (takes ~2 hours).
A Meta Ads 'Andromeda Auditor' that analyzes Advantage+ campaign settings and automatically outputs structured creative briefs for designers based on performance gaps.
We will build a specialized prompt in Claude that accepts screenshots of Meta Advantage+ campaign settings alongside ad creative performance metrics. We will record a screen recording showing the AI analyzing the inputs, identifying optimization mistakes, and generating a formatted markdown creative brief ready for a designer.
screen_recording quick — Requires drafting the system prompt, gathering a sample Meta Ads screenshot/performance metrics, and recording a brief video walkthrough of the execution (takes ~1 hour).
Jul 16, 13:54 linkedin_author_expansion ai_marketing high 83.2 processed

I cleaned up my Claude projects this week. Buried in one folder were dozens of prompts I’d written

https://www.linkedin.com/posts/rananjayraj_i-cleaned-up-my-claude-projects-this-week-activity-7477707639188123649-bGZ6?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGhtqnkBlAH9R4sBJ5YIgAmHDEXmcztJvdk

Stop wasting time copy-pasting repetitive prompts for positioning, ICP extraction, and hook testing. Sibonix should immediately convert these seven specific marketing workflows into permanent Claude Skills to automate our repeat work. The immediate next step is to comment 'SKILLS' on this post to secure the creator's pre-built Skill files and integrate them into our AI agent systems.

Read full content
I cleaned up my Claude projects this week. Buried in one folder were dozens of prompts I’d written over the last year. Most of them are gone now. Not because they stopped working, but because I kept using the same ones until they became Claude Skills. These are the seven prompts I still come back to. 1. The Positioning Audit “You’re a senior strategy consultant. Read my website [drag files]. Identify the 3 positioning statements that are competing. Recommend which to keep.” I rarely type this anymore. The Positioning Audit Skill handles it. 2. The ICP Extractor “From this [interview transcript / LinkedIn bio / sales call notes], extract: their role, their pain, their desired outcome, and the 3 words they use to describe the problem.” This became a Customer Research Personas Skill because I was using it almost every week. 3. The Hook Tester “Here are 5 LinkedIn hook drafts. Rank them on (1) specificity, (2) stakes, (3) curiosity gap. Rewrite the bottom 3 to match the top 2.” I still check the output, but the Virality Predictor Skill does most of the work now. 4. The Brief Compressor “Here’s a 12-page campaign brief. Rewrite as a 1-page visual brief: objective, audience, message, 3 proof points, CTA.” One thing I like is that it flags missing information instead of filling the gaps. 5. The Gap Finder “Compare [competitor’s] positioning to [ours]. Where are they strong and we’re silent? Where are we strong and they’re silent?” The Skill version also ranks which gaps are actually worth going after. 6. The Stakeholder Translator “Rewrite this strategy doc 3 times: once for the CFO (focus on ROI and risk), once for the CEO (focus on growth and story), once for the board (focus on defensibility).” The board version is automated. I still rewrite the CFO and CEO versions myself. 7. The Prompt Refiner “I just asked you [prompt]. The output was [paste]. What information would have made your answer 10x better? Rewrite the improved prompt.” Ironically, this prompt helped improve all the others. Looking back, I don’t think I’ve become better at writing prompts. I’ve become better at spotting repeat work. If I find myself using the same prompt more than a couple of times a month, it’s usually a sign that it should become a Skill instead. Comment SKILLS and I’ll send over the actual Skill files that replaces these prompts. Like, save & re-share ♻️ with other marketers & founder who might need it.
Ideas (2)
Converting repetitive manual marketing prompts (like ICP extraction and positioning audits) into permanent Claude Project 'Skills' with system-level instructions to eliminate copy-pasting.
We will build a Claude Project pre-configured with custom 'Skills' for ICP extraction and positioning audits, upload a raw customer interview transcript and a competitor homepage, and record a screen walkthrough showing Claude instantly producing structured marketing assets without any manual prompting.
screen_recording moderate — Requires setting up a Claude Project, writing two system instructions, preparing real sample assets, and recording the execution (approx. 2 hours).
Implementing a self-optimizing 'Prompt Refiner' skill that analyzes a mediocre marketing output, identifies missing context, and automatically rewrites the prompt for a 10x better result.
We will run a basic LinkedIn hook generation prompt, pass its output through the Prompt Refiner skill to extract missing variables (stakes, specificity), and present a side-by-side comparison of the initial generic hooks versus the highly targeted hooks generated by the refined prompt.
before_after_comparison quick — Requires running two Claude prompt generations and formatting the resulting prompts and outputs into a clear comparative layout (approx. 1 hour).
Jul 16, 13:54 linkedin_author_expansion data medium 58.2 processed

באמצע יום שלישי חפרתי לצ'אטבוט AI עד שהשרת שלו כמעט קרס. שילמתי 97 שקל למנוי. לפי כמות הטוקנים ששרפת

https://www.linkedin.com/posts/or-ben-haimmm_%D7%91%D7%90%D7%9E%D7%A6%D7%A2-%D7%99%D7%95%D7%9D-%D7%A9%D7%9C%D7%99%D7%A9%D7%99-%D7%97%D7%A4%D7%A8%D7%AA%D7%99-%D7%9C%D7%A6%D7%90%D7%98%D7%91%D7%95%D7%98-ai-%D7%A2%D7%93-%D7%A9%D7%94%D7%A9%D7%A8%D7%AA-activity-7479083695686090752-QqlB?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGgye3IBjBVnADtgU6W7q2Tyl9efce8ooqw

This post highlights a major risk for Sibonix's AI agent systems: flat-rate pricing is highly vulnerable to 'heavy users' who burn excessive tokens and destroy margins. We must stop relying on average usage metrics and actively track individual client resource consumption to identify unprofitable accounts. Our next step is to audit our AI agent delivery costs to ensure high-volume users aren't quietly draining our profitability.

Read full content
באמצע יום שלישי חפרתי לצ'אטבוט AI עד שהשרת שלו כמעט קרס. שילמתי 97 שקל למנוי. לפי כמות הטוקנים ששרפתי להם ב-AWS, הבעלים של ה-SaaS הזה כנראה בוכה עכשיו. אני ה-Heavy User הקלאסי. העלוקה של הזנב הארוך. (כן, גם כשאני לא במשרד אני חושב כמו דאטה אנליסט). זה הצחיק אותי. עד שנזכרתי שזה בדיוק מה שקורה בעולם האמיתי. כשהייתי בבנק, היה לנו לקוח אחד כזה. על הנייר? לקוח VIP. בפועל? שאב את כל המשאבים של המחלקה. כולם הסתכלו על שורת ההכנסות ממנו והיו מבסוטים. אני הסתכלתי על כמות שעות העבודה שהוא שרף לנו, וחשבתי: הלקוח הזה בכלל לא רווחי מרוב משאבים שאנחנו שופכים עליו. חברות מתות על ממוצעים. הממוצע אומר שהכל בסדר. אבל הממוצע מסתיר את הלקוחות ששותים לכם את הרווחיות בקשית. ממוצעים זה לעצלנים. תחפשו את העלוקות. תמונה של הצ״ט ליחס.
Ideas (2)
Flat-rate pricing for AI agents is highly vulnerable to 'heavy users' whose excessive token consumption quietly destroys margins under the guise of 'average' usage metrics.
Build a live resource-tracking dashboard using Langfuse and Looker Studio that monitors API token costs per client in real-time, then run a simulation showing how a single high-volume agent run spikes costs and drops a simulated client's margin below profitability.
live_workflow_walkthrough moderate — Requires setting up Langfuse tracing on a test AI agent, connecting it to Looker Studio, and simulating a high-volume run to capture the data.
To protect profitability from heavy users, AI agent architectures must implement active cost-guardrails rather than relying on retrospective audits.
Build a Python-based middleware router for our AI agents that monitors a client's cumulative monthly spend and dynamically downgrades the LLM engine (e.g., from GPT-4o to GPT-4o-mini) or throttles usage once they cross a specific cost threshold, showing the code and a screen recording of the threshold trigger in action.
screen_recording substantial — Requires writing custom routing logic in Python, integrating a database to store running token tallies, and recording the system switching models mid-run.
Jul 16, 13:54 linkedin_author_expansion ai_marketing medium 58.2 processed

הייתי בטוח שפיצחתי את השיטה. (ספוילר: לא פיצחתי כלום). בניתי מטריצה שלמה. צבא קטן של סוכני AI שאמורי

https://www.linkedin.com/posts/or-ben-haimmm_%D7%94%D7%99%D7%99%D7%AA%D7%99-%D7%91%D7%98%D7%95%D7%97-%D7%A9%D7%A4%D7%99%D7%A6%D7%97%D7%AA%D7%99-%D7%90%D7%AA-%D7%94%D7%A9%D7%99%D7%98%D7%94-%D7%A1%D7%A4%D7%95%D7%99%D7%9C%D7%A8-%D7%9C%D7%90-activity-7477276170372190208-zY8T?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGgymj8BRYvwSJ_ah8Gf5Lt2SZlSs63CG6w

This experiment proves that standard AI agents are too polite and linear to accurately simulate the chaotic, peer-influenced dynamics of a human focus group. For Sibonix, this is a warning not to sell basic multi-agent setups as a complete replacement for human market research. If we build synthetic focus groups, we must explicitly program agents to persuade, disagree, and change their minds dynamically rather than just reading from their prompt-defined scripts.

Read full content
הייתי בטוח שפיצחתי את השיטה. (ספוילר: לא פיצחתי כלום). בניתי מטריצה שלמה. צבא קטן של סוכני AI שאמורים לייצר קבוצת מיקוד סינתטית. כל סוכן קיבל פרסונה, נקודת מוצא, דפוסי התנהגות. המטרה? לחסוך את הכאב ראש של להביא אנשים אמיתיים לחדר עם מראה דו-כיוונית ובורקס, ולתת למודלי שפה לעשות את זה מהר ובזול. ואז הרצתי את הטסט מול דאטה של בני אדם אמיתיים (הבייסליין). ושם קלטתי את הפדיחה. הסוכנים שלי היו יותר מדי... מנומסים. הם חשבו בצורה לינארית. כל סוכן נצמד לנקודת המוצא שלו כמו ילד טוב שמקריא מהדף. הם לא ידעו לחשוב עצמאית תוך כדי תנועה. אפס סטייה מהפרומפט. ואז עצרתי ושאלתי את עצמי: רגע, ככה בני אדם מתנהגים בקבוצת מיקוד? הם פשוט יושבים, אומרים את דעתם ולא זזים ממנה מילימטר? או שיש שם כאוס, שכנועים, ומישהו שמשנה את דעתו רק כי ההוא לידו דיבר ממש בביטחון? הדאטה שלי מראה ש-AI חושב לינארית. הוא לא יודע לזייף דינמיקה אנושית. אבל פה אני צריך אתכם, אנשי מוצר, UX ומחקר שיושבים בחדרים האלה: מה באמת קורה בדינמיקה של קבוצת מיקוד אנושית? (חוץ מהבורקס). (תמונת בורקס מצורפת לרפרנס, כי עם כל הכבוד לטכנולוגיה, את זה מודל שפה עוד לא יודע לייצר).
Ideas (2)
Standard AI agent simulations lack peer-influence dynamics, resulting in polite, linear discussions where agents never change their minds or succumb to group pressure.
We will build a multi-agent debate simulation in LangGraph where each agent is assigned a dynamic 'conviction score' (1-10) in their state. We will run a comparative test on a new product positioning angle, showing a side-by-side run of a standard agent group (which remains stagnant) versus our dynamic group, where a highly confident agent successfully persuades others to update their conviction scores and change their initial stance.
before_after_comparison moderate — Requires setting up a custom LangGraph state machine with 3-4 agents, tracking their internal conviction variables, and logging the output JSON.
Simulating chaotic, non-linear human focus groups requires agents to have explicit persuasion thresholds and social susceptibility parameters rather than just static system prompts.
We will build a Streamlit dashboard that visualizes a synthetic focus group discussing a controversial ad creative in real-time. The screen recording will show a live line chart tracking the 'group consensus index' as a simulated skeptic agent's defense breaks down under the influence of two enthusiast agents.
screen_recording substantial — Requires building a Streamlit frontend, integrating it with an asynchronous OpenAI assistant thread, and mapping real-time sentiment analysis to a chart.
Jul 16, 13:54 linkedin_author_expansion ai_marketing medium 58.2 processed

מהיום אפשר להריץ את Claude ישירות בתוך Azure ההכרזה יצאה היום ביחד עם Microsoft, ומה שזמין עכשיו זה

https://www.linkedin.com/posts/amitshafnir_%D7%9E%D7%94%D7%99%D7%95%D7%9D-%D7%90%D7%A4%D7%A9%D7%A8-%D7%9C%D7%94%D7%A8%D7%99%D7%A5-%D7%90%D7%AA-claude-%D7%99%D7%A9%D7%99%D7%A8%D7%95%D7%AA-%D7%91%D7%AA%D7%95%D7%9A-azure-activity-7477465719518097408-gJ-o?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGgyZFQBT5CvsekxGLqhOKNcJbM4p9Nvf8Y

This integration is a massive win for enterprise clients locked into Azure agreements who can now burn their existing cloud budget on Anthropic's Opus 4.8 and Haiku 4.5. Sibonix should leverage this to deploy AI agents for clients demanding strict data sovereignty and unified billing. The concrete next step is to test the Messages API within Microsoft Foundry to verify if prompt caching and extended thinking perform seamlessly compared to native Anthropic endpoints.

Read full content
מהיום אפשר להריץ את Claude ישירות בתוך Azure ההכרזה יצאה היום ביחד עם Microsoft, ומה שזמין עכשיו זה Opus 4.8 ו-Haiku 4.5 דרך ה-Messages API, בתוך Microsoft Foundry. כל מי שכבר עובד על הענן של מיקרוסופט יכול פשוט לקרוא לקלוד מאותו מקום שבו הוא מנהל את כל שאר השירותים שלו. ההזדהות, החיוב והניהול עוברים דרך המערכת הקיימת ב-Azure, חשבונית אחת מאוחדת, בלי ספק נפרד וניהול נפרד. לקוחות עם Enterprise Agreement מול מיקרוסופט יכולים לקזז את השימוש בקלוד מול ההתחייבות התקציבית שכבר יש להם ל-Azure. כלומר התקציב שהובטח לענן הולך עכשיו גם על מודלים של Anthropic. יש שתי דרכים להריץ את זה. אפשר לבחור באירוח על Azure עצמו, שרץ בתוך סביבת הענן שלכם עם אזור דאטה אמריקאי לעמידה בדרישות ריבונות מידע, ואפשר לבחור באירוח על אנתרופיק, שנותן את כל סט הפיצ׳רים של ה-API ואת המודלים שעוד לא עלו ל-Azure. בשני המקרים אנתרופיק היא זאת שמפעילה את ההרצה בפועל. מתחת לכל זה רצים מעבדי NVIDIA GB300, כשאנתרופיק מנהלת את תשתית ההרצה עצמה. כבר עכשיו נתמכים prompt caching ו-extended thinking, ועוד פיצ׳רים בדרך.
Ideas (2)
Running Anthropic's Claude (Opus 4.8 and Haiku 4.5) directly within Azure via Microsoft Foundry using the Messages API, allowing enterprise clients to offset costs using existing Azure Enterprise Agreements.
We will build a multi-agent content generation workflow using Microsoft AutoGen, configure it to call Claude Haiku 4.5 via the Azure Microsoft Foundry endpoint, and show a live walkthrough of the Azure portal configuration, the API connection string, and the unified billing dashboard tracking the resource spend.
live_workflow_walkthrough substantial — Requires active Azure subscription with Microsoft Foundry access, deploying the Claude endpoints, and building a multi-agent Python orchestration script.
Native support for Claude's advanced features like prompt caching and extended thinking directly within the Azure-integrated environment.
We will write a comparative test script that feeds a massive 50,000-token marketing brand guideline document into Claude Opus 4.8 on Azure, comparing the processing speed (latency) and token billing metrics with prompt caching turned on versus turned off.
before_after_comparison moderate — Requires access to Azure's Claude API, a Python script to measure execution times, and a structured mock dataset of 50k tokens.
Jul 16, 13:54 linkedin_author_expansion ai_marketing high 83.2 processed

זהו, ChatGPT 5.6 יצא, והוא לא פחות גדול מ-Fable. אבל הרבה יותר זול. המשפחה מגיעה בשלושה גדלים: Sol

https://www.linkedin.com/posts/amitshafnir_%D7%96%D7%94%D7%95-chatgpt-56-%D7%99%D7%A6%D7%90-%D7%95%D7%94%D7%95%D7%90-%D7%9C%D7%90-%D7%A4%D7%97%D7%95%D7%AA-%D7%92%D7%93%D7%95%D7%9C-%D7%9E-fable-activity-7481091345625894913-_IUg?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGgyocEBLjipOn7JBXv2sz5fqM1RTEOzJOU

This release is highly relevant for Sibonix's agent builds as the new Programmatic Tool Calling and native Multi-agent Responses API directly address token waste and manual orchestration. We should immediately test Sol, Terra, and Luna in our API workflows to leverage the significantly lower token costs. However, we must proceed with caution and monitor for production friction due to OpenAI's acknowledged aggressive safeguard blocks.

Read full content
זהו, ChatGPT 5.6 יצא, והוא לא פחות גדול מ-Fable. אבל הרבה יותר זול. המשפחה מגיעה בשלושה גדלים: Sol הדגל, Terra המאוזן, ו-Luna הזול והמהיר. שלושתם על אותו רעיון מרכזי: יותר תוצאה על כל טוקן. מכירים את התחושה שאתם משלמים על שרשרת סוכנים שמדברת עם עצמה חצי שעה ואז מחזירה משהו בינוני? כאן OpenAI טוענים שהם תקפו בדיוק את זה. סול עם max reasoning מגיע ל-80 באינדקס של Artificial Analysis, אבל עם פחות מחצי הטוקנים ובכשליש מהעלות של המודל הבא בתור. גם טרה ולונה עוקפים מודלים גדולים בכמה מהמדדים בסביבות רבע מהעלות. מתחת למספרים יש שתי יכולות שמעניינות מהנדסים יותר מהגרפים. הראשונה היא Programmatic Tool Calling: המודל כותב ומריץ JavaScript בתוך Runtime מבודד כדי לעשות אורקסטרציה בין קריאות לכלים, לסנן תוצאות ביניים ולהחליט מה הצעד הבא, בלי שכל תשובה, משימוש בכלי, תחזור דרך המודל. פחות סבבים, פחות טוקנים, פחות אורקסטרציה ידנית. הרבה מהחיסכון שהם מוכרים מגיע בדיוק מכאן. השנייה היא Multi-agent בבטא ב-Responses API: בקשה אחת מפצלת סאב-אייג׳נטים במקביל ומאחדת את התוצאות. זה בעצם מה שמפעיל את הגדרת ultra שמריצה ארבעה סוכנים כברירת מחדל. ויש חלק שראוי לזהירות. אותם מודלים חזקים יותר גם בסייבר ובביולוגיה, ולכן הם מגיעים עם שכבת סייפגארד אגרסיבית. בקשות מסוימות ייחסמו או ייעצרו באמצע, במיוחד באזורים דו-שימושיים שבהם הגנה והתקפה נראות אותו דבר בהתחלה. חברת OpenAI, אומרים בגלוי שהם כיוונו שמרני ושחלק מהחסימות יתפסו עבודה לגיטימית. מי שעבד עם חסמים כאלה יודע כמה חיכוך זה יכול להכניס לפרודקשן. המחיר ממש טוב יחסית לאנת׳רופיק, לכל מיליון טוקנים: סול 5/30 דולר, טרה 2.5/15, לונה 1/6. זמין כבר היום ב-API, ב-ChatGPT וב-Codex. קיצר, שווה ניסיון בהקדם 😊
Ideas (7)
We are migrating our marketing agent architectures to ChatGPT 5.6 to eliminate token waste caused by back-and-forth tool loops.
Most marketing agents spend 80% of their token budget talking to themselves.
linkedin_post
The aggressive safeguard blocks in ChatGPT 5.6 present a major production risk for live marketing campaigns that we are currently stress-testing.
OpenAI's new ChatGPT 5.6 models will halt your automated marketing workflows if you do not bypass their safety blocks first.
carousel
Native multi-agent execution in ChatGPT 5.6 allows us to bypass slow custom orchestration layers for faster marketing content generation.
We are swapping out our custom multi-agent orchestration for OpenAI's native Responses API.
short_video_script
Programmatic Tool Calling, where the model writes and runs JavaScript in an isolated runtime to orchestrate tool calls and filter intermediate results without returning every step to the model.
Build a benchmark script that compares a traditional multi-step API fetching workflow (using standard sequential LLM tool calls) against the new Programmatic Tool Calling feature. We will run both setups through a marketing lead enrichment task and show a side-by-side comparison of the total API roundtrips, token consumption, and execution speed.
before_after_comparison
The native Multi-agent Responses API in Beta, which parallelizes sub-agents from a single request and automatically consolidates their outputs.
Build a workflow that executes a comprehensive competitor analysis by triggering the new Responses API under its 'ultra' configuration to run four specialized sub-agents (SEO, pricing, positioning, and ad copy) simultaneously. We will record the execution trace to show how a single API call orchestrates these parallel streams and outputs a unified marketing strategy report.
live_workflow_walkthrough
Programmatic Tool Calling executing JavaScript in an isolated runtime to orchestrate tools and filter intermediate results without model roundtrips.
We will build a competitor pricing scraper workflow that queries three search APIs and parses raw JSON. We will compare a traditional sequential tool-calling loop (sending every raw JSON payload back to the LLM) against a script using the new Programmatic Tool Calling API running JavaScript locally, highlighting the exact token savings and latency reduction.
before_after_comparison moderate — Requires setting up a Node.js test script using the new OpenAI 5.6 API to run both orchestration methods and logging the API token metrics.
Native Multi-agent Responses API that splits parallel sub-agents from a single request and merges their outputs natively.
We will build a multi-channel ad copy generator that simultaneously drafts LinkedIn, Twitter, and email copy. We will write a Python script using the new Responses API with the 'ultra' setting enabled, record a walkthrough of the single-payload request, and show how the parallel sub-agent outputs are compiled into a unified structured JSON output.
live_workflow_walkthrough moderate — Requires OpenAI API access to the new multi-agent Responses API, writing a script with structured output schemas, and capturing the console execution.
Jul 16, 13:54 linkedin_author_expansion ai_marketing high 83.2 processed

המשפחה של GPT-5.6 עם שלושת המודלים שלה, Sol ו-Terra ו-Luna, עולה לאוויר לכולם ביום חמישי הקרוב. בסו

https://www.linkedin.com/posts/amitshafnir_%D7%94%D7%9E%D7%A9%D7%A4%D7%97%D7%94-%D7%A9%D7%9C-gpt-56-%D7%A2%D7%9D-%D7%A9%D7%9C%D7%95%D7%A9%D7%AA-%D7%94%D7%9E%D7%95%D7%93%D7%9C%D7%99%D7%9D-%D7%A9%D7%9C%D7%94-sol-activity-7480473535954677760-dibD?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGgyocEBLjipOn7JBXv2sz5fqM1RTEOzJOU

OpenAI is launching the GPT-5.6 model family—Sol, Terra, and Luna—to the public this Thursday. Sibonix must immediately prepare to test these models, leveraging Terra for cost-effective agent tasks and Sol for complex research and coding. This launch is a critical priority to determine if these models outperform current market alternatives.

Read full content
המשפחה של GPT-5.6 עם שלושת המודלים שלה, Sol ו-Terra ו-Luna, עולה לאוויר לכולם ביום חמישי הקרוב. בסוף יוני, היא יצאה בתור גישה מוגבלת, רק בערך 20 ארגונים נבחרים קיבלו אותה דרך ה-API ו-Codex בלבד. ביום חמישי זה נגמר, המודלים נפתחים לציבור הרחב, ובמקביל OpenAI מרחיבה כבר עכשיו את הגישה המוקדמת לכל העולם. המודל דגל של המשפחה הוא Sol, המודל החזק לבעיות הקשות באמת בקוד, במחקר ובסייבר. באמצע נמצאת Terra המאוזנת, ביצועים של ג׳יפיטי 5.5 בערך בחצי מחיר. הזולה והמהירה היא Luna, בשביל נפח גדול ומשימות יומיומיות. יום חמישי נגלה אם יש כאן תחרות לפאבל, ואם כן, הולך להיות מעניין…
Ideas (9)
The release of GPT-5.6 Terra and Luna allows us to scale automated marketing agents at a fraction of current API costs.
OpenAI is about to slash the cost of running enterprise AI agent systems.
linkedin_post
We are putting GPT-5.6 Sol to the test this Thursday to determine if it replaces Claude for our advanced research and coding agents.
We are benchmarking OpenAI's new Sol model the hour it drops this Thursday.
carousel
The Sol, Terra, and Luna trifecta matches the exact three-tier architecture Sibonix uses to build cost-efficient marketing agent systems.
This is how we structure model tiers to keep AI agent systems fast and cheap.
short_video_script
GPT-5.6 Sol is designed as the flagship model for solving highly complex coding, cyber, and research problems.
We will build a benchmarking pipeline that feeds Sol a legacy codebase refactoring challenge containing nested asynchronous API calls and rate-limiting issues. We will run the code generation live and compare the debugging output side-by-side with Claude 3.5 Sonnet to evaluate logic accuracy.
before_after_comparison
GPT-5.6 Terra delivers the performance of GPT-5.5 at approximately half the operational cost.
We will build a multi-agent lead enrichment workflow using CrewAI, running 100 test runs through both GPT-5.5 and GPT-5.6 Terra. We will display a live dashboard comparing the total API spend and output quality scores of both models to verify the 50% cost-savings claim.
live_workflow_walkthrough
GPT-5.6 Luna is a low-cost, high-speed model optimized for processing high-volume daily tasks.
We will build an automated content moderation and categorization engine that ingests 500 simulated user comments. We will record a screen walkthrough showing Luna processing the queue in real-time, highlighting the latency per request and the final API cost for the batch.
screen_recording
The flagship Sol model is designed for complex research, coding, and cybersecurity tasks.
We will build an automated vulnerability-patching agent using the Sol API to scan a complex, multi-file repository, identify security flaws, and write pull requests to fix them, comparing the success rate and code quality directly against Claude 3.5 Sonnet.
before_after_comparison substantial — Requires API access to the Sol model, setting up a target codebase with known vulnerabilities, and running comparative evaluations.
The Terra model provides balanced performance (roughly GPT-5.5 level) at half the price, optimized for cost-effective agent tasks.
We will build a multi-agent lead-enrichment pipeline in LangGraph, run 500 leads through it using Terra, and show a cost-and-accuracy comparison dashboard comparing its performance and API spend against GPT-4o.
before_after_comparison moderate — Requires building the LangGraph pipeline, running a batch of 500 leads, and compiling the API token cost metrics.
The Luna model is optimized for high-volume, low-latency, and cost-sensitive everyday tasks.
We will build a real-time social listening agent in Make.com that monitors live RSS feeds, uses Luna to instantly summarize and categorize 100 incoming articles in parallel, and record a live execution to show the latency and total cost.
live_workflow_walkthrough moderate — Requires setting up the RSS trigger in Make.com, connecting the Luna API, and recording the execution speed metrics.
Jul 16, 13:54 linkedin_author_expansion ai_marketing high 83.2 processed

מריצים סוכנים על Vercel? היום הם יכולים כבר להתקשר אליכם! וורסל הוסיפה תמיכה מלאה בסוכני קול בזמן א

https://www.linkedin.com/posts/amitshafnir_%D7%9E%D7%A8%D7%99%D7%A6%D7%99%D7%9D-%D7%A1%D7%95%D7%9B%D7%A0%D7%99%D7%9D-%D7%A2%D7%9C-vercel-%D7%94%D7%99%D7%95%D7%9D-%D7%94%D7%9D-%D7%99%D7%9B%D7%95%D7%9C%D7%99%D7%9D-%D7%9B%D7%91%D7%A8-activity-7477591702321414147-FM3z?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGgyZFQBT5CvsekxGLqhOKNcJbM4p9Nvf8Y

Vercel's new real-time voice agent support in AI SDK 7 is a massive upgrade for building secure, low-latency conversational agents that can call external tools mid-call. Because it secures API keys via server-side token generation and charges no extra platform fees, Sibonix should immediately test this capability using the `useRealtime` hook or the browser playground. This allows us to deploy production-ready, voice-enabled AI agents running on OpenAI's gpt-realtime-2 with built-in cost controls.

Read full content
מריצים סוכנים על Vercel? היום הם יכולים כבר להתקשר אליכם! וורסל הוסיפה תמיכה מלאה בסוכני קול בזמן אמת שרצים ישירות דרך ה-Gateway. המודל מקשיב למשתמש, מעבד את התשובה, ואז מדבר חזרה בלייב, עם זמן תגובה נמוך. באמצע השיחה הוא יכול לעצור, לקרוא לכלי חיצוני בשביל לבדוק נתון או לבצע פעולות, ולחזור לדבר. זתומרת זה כבר לא הקראה של איזשהו פרומפט מוכן, אלא סוכן שמנהל שיחה אמיתית. מעבר לזה נכנסו גם המרת טקסט לדיבור עם בחירת קולות ופורמטים, תמלול של אודיו לטקסט מקבצים, מ-base64 או מכתובות ישירות. מבחינת אבטחה, במקום לחשוף את מפתח ה-API בדפדפן, יש תהליך בצד השרת שמייצר טוקנים קצרי טווח, והדפדפן רק מנהל את חיבור ה-WebSocket ואת האודיו. ככה המפתח האמיתי אף פעם לא יוצא מהשרת. כל זה יושב בתוך AI SDK 7 (החדש), עם אותו ניתוב מאוחד, אותה שכבת מעקב ואותן בקרות הוצאה שכבר קיימות ב-Gateway, בלי תוספת מחיר ובלי דמי פלטפורמה. אפשר לעבוד מול הקוד ישירות דרך הוק בשם useRealtime, או פשוט להיכנס ל-playground בדפדפן ולבדוק בלי לכתוב שורת קוד. בינתיים הכל בבטא, ונכון לעכשיו זה רץ על gpt-realtime-2 של OpenAI יחד עם מודלים של ספקים נוספים.
Ideas (7)
Vercel's zero-fee real-time voice SDK removes the expensive middleware layer, allowing growing brands to deploy high-speed voice agents directly.
Voice commerce just lost its expensive middleman.
linkedin_post
The ability to run mid-call tool executions with zero latency means voice agents can now handle live database lookups and checkouts during a conversation.
Your next inbound sales rep runs on gpt-realtime-2 with zero platform fees.
carousel
Server-side token generation in AI SDK 7 solves the security risk of client-side API leaks for customer-facing brand agents.
You can now deploy web-based voice agents without exposing your OpenAI API keys to the browser.
short_video_script
Vercel AI SDK 7 introduces the `useRealtime` hook and server-side ephemeral token generation, allowing developers to run secure, low-latency browser-based voice agents without exposing raw API keys to the client side.
We plan to build a Next.js proof-of-concept application using the Vercel AI SDK 7 that connects to OpenAI's real-time model. We will show a split-screen walkthrough: on one side, the server-side route generating the ephemeral token, and on the other, the browser's Network tab proving that only the short-lived session token is exposed during the active WebSocket connection.
live_workflow_walkthrough
Vercel's real-time voice integration supports mid-conversation tool calling, enabling voice agents to pause mid-sentence, fetch external data or trigger API actions, and resume speaking dynamically.
We plan to build a voice-based customer support agent that handles shipping inquiries. We will record a screen capture of a live user conversation where the agent pauses to execute a mock shipping database lookup tool mid-call, highlighting the terminal logs to show the real-time transition from voice input to JSON tool execution and back to synthetic voice output.
screen_recording
Vercel AI SDK 7's secure real-time voice agent capability using server-side token generation to run OpenAI's gpt-realtime-2 with mid-call tool execution.
Build a Next.js prototype using the `useRealtime` hook that generates short-lived session tokens server-side to protect the OpenAI API key. We will program a voice-activated marketing assistant that pauses mid-conversation to query a live mock CRM tool and verbally read back lead statuses, recording a live walk-through of the WebSocket connection and the voice interaction.
live_workflow_walkthrough substantial — Requires building a Next.js app with AI SDK 7, configuring OpenAI real-time API access, writing server-side token exchange logic, and implementing a mock CRM tool.
Vercel AI Gateway's unified routing, cost tracking, and budget controls for real-time voice streams without extra platform fees.
Set up a voice stream through Vercel's AI Gateway and run a simulated customer support call using the browser playground. We will capture a screen recording of the Vercel dashboard displaying the real-time cost tracking, latency metrics, and budget limits applied specifically to that live gpt-realtime-2 audio session.
screen_recording moderate — Requires setting up a Vercel AI Gateway instance, configuring OpenAI API keys, and executing a test voice stream to generate dashboard metrics.
Jul 16, 13:54 linkedin_author_expansion ai_marketing medium 58.2 processed

אחרי שלושה שבועות של השבתה, Fable 5 חוזר מהיום לכל העולם. נחזור רגע אחורה. ב-12 ביוני ממשלת ארצות ה

https://www.linkedin.com/posts/amitshafnir_%D7%90%D7%97%D7%A8%D7%99-%D7%A9%D7%9C%D7%95%D7%A9%D7%94-%D7%A9%D7%91%D7%95%D7%A2%D7%95%D7%AA-%D7%A9%D7%9C-%D7%94%D7%A9%D7%91%D7%AA%D7%94-fable-5-%D7%97%D7%95%D7%96%D7%A8-activity-7477959965719072768-HRbc?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGgyZFQBT5CvsekxGLqhOKNcJbM4p9Nvf8Y

Fable 5 is back online globally across the Claude Platform, Claude Code, and Cowork after a three-week security shutdown. Sibonix should immediately test this restored model for our AI agent builds, keeping in mind that free access within paid plans is capped at half the weekly quota until the 7th of the month before transitioning to credits. The security patch successfully blocks the code-exploit vulnerabilities that triggered the ban, making it safe to reintegrate into our workflows.

Read full content
אחרי שלושה שבועות של השבתה, Fable 5 חוזר מהיום לכל העולם. נחזור רגע אחורה. ב-12 ביוני ממשלת ארצות הברית הטילה פיקוח ייצוא על Fable 5 ועל Mythos 5, ודרשה להגביל את הגישה אליהם לאזרחים זרים, בתוך ארצות הברית ומחוצה לה. ל-Anthropic לא הייתה דרך אמינה לוודא אזרחות בזמן אמת, אז הם פשוט השביתו את שני המודלים לכולם באותו רגע. מה שהדליק את כל זה היה גילוי של חוקרים מ-Amazon. הם מצאו דרך לעקוף את מנגנוני ההגנה של Fable 5 ולגרום לו לאתר חולשות אבטחה בקוד, ובמקרה אחד המודל אפילו יצר קוד שמדגים איך לנצל חולשה מסוימת. אבל הבדיקה של אנתרופיק העלתה משהו אחר. היכולת הזאת בכלל לא ייחודית להם. כל מודל שהם בדקו הצליח להפיק בדיוק את אותה הדגמה, כולל גרסאות של Opus, GPT-5.5 ו-Kimi K2.7. במקביל הם שחררו שכבת אבטחה חדשה שחוסמת את הטכניקה הספציפית שדיווחו עליה בדוח, בלמעלה מ-99% מהמקרים. אז בגדול, מהיום Fable 5 זמין שוב בכל העולם, ב-Claude Platform, ב-Claude[.]ai, ב-Claude Code וב-Cowork. שימו לב! עד השביעי לחודש Fable 5 כלול עד חצי ממכסת השימוש השבועית בתוכניות בתשלום, ואחר כך הגישה עוברת לקרדיטים. מעבר לחזרה עצמה, אנתרופיק סגרה עם אמזון, Microsoft ו-Google מסגרת משותפת להערכת החומרה של פריצות מהסוג הזה, והתחייבה לשיתוף פעולה עמוק יותר עם הממשל, כולל גישה מוקדמת למודלים לבדיקה עוד לפני שהם יוצאים החוצה.
Ideas (2)
Fable 5 is back online in Claude Code with a new security layer designed to block code-exploit vulnerabilities in over 99% of cases.
We will set up a testing environment using Claude Code powered by the restored Fable 5 model, feed it a mock application containing a known security vulnerability, and document whether it successfully generates a secure patch while safely blocking requests to write an exploit payload.
live_workflow_walkthrough moderate — Requires setting up Claude Code, creating a mock vulnerable script, and running test prompts to observe the security guardrails in real-time.
Fable 5's access model within paid plans is capped at half the weekly quota until the 7th of the month before transitioning to credits.
We will write a Python monitoring script that connects to the Anthropic API, tracks token consumption specifically for Fable 5 runs, and triggers a Slack alert when our team reaches 45% of the weekly quota to manage the transition to credit-based billing.
screen_recording quick — Requires writing a short Python script to fetch Anthropic API usage data and testing the Slack webhook integration.
Jul 16, 13:54 linkedin_author_expansion ai_marketing high 83.2 processed

עכשיו הסוכן שלכם יכול לקבל מייל, לקרוא אותו ולענות. לבד. אג׳נטמייל (AgentMail) הצטרפה השבוע ל-Marke

https://www.linkedin.com/posts/amitshafnir_%D7%A2%D7%9B%D7%A9%D7%99%D7%95-%D7%94%D7%A1%D7%95%D7%9B%D7%9F-%D7%A9%D7%9C%D7%9B%D7%9D-%D7%99%D7%9B%D7%95%D7%9C-%D7%9C%D7%A7%D7%91%D7%9C-%D7%9E%D7%99%D7%99%D7%9C-%D7%9C%D7%A7%D7%A8%D7%95%D7%90-%D7%90%D7%95%D7%AA%D7%95-activity-7483038693096869888-_li9?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGgyocEBLjipOn7JBXv2sz5fqM1RTEOzJOU

AgentMail solves a major infrastructure headache for Sibonix by handling email server setup, spam deliverability, and thread management for AI agents out of the box. Since it integrates directly with Vercel and triggers real-time workflows via Webhooks and WebSockets, we should immediately test this tool to deploy autonomous email-handling agents. This allows us to easily provision real inboxes for our agents via a simple API.

Read full content
עכשיו הסוכן שלכם יכול לקבל מייל, לקרוא אותו ולענות. לבד. אג׳נטמייל (AgentMail) הצטרפה השבוע ל-Marketplace של Vercel, וזה בעצם אומר שאתם נותנים לסוכן AI תיבת מייל אמיתית משלו. הוא שולח, מקבל ומגיב על מיילים כמו כל אחד מאיתנו, רק שמאחורי הקלעים אג׳נטמייל מטפלת בכל הכאב ראש של שרתי המייל וההגעה לתיבה הנכונה (ולא לספאם). מקימים תיבות ודומיינים דרך ה-API. מקבלים טיפול בשרשורי מיילים, קבצים מצורפים והגעה לתיבה מהקופסה. מפעילים תהליכים אוטומטיים של הסוכן דרך Webhooks ו-WebSockets, כלומר ברגע שנכנס מייל הסוכן קופץ לפעולה בזמן אמת. ההתקנה היא שורה אחת בטרמינל. vc i agentmail אתם קולטים לאן העולם הולך? 😁 סוכן שמנהל לכם את תיבת המייל, מסנן, עונה על הפשוטים ומעביר אליכם רק את מה שבאמת דורש אתכם.
Ideas (8)
Sibonix is adopting AgentMail to eliminate the custom backend infrastructure normally required to give AI marketing agents deliverable email addresses.
Giving an AI agent a functioning email address used to require hours of server configuration and spam filter testing.
linkedin_post
We are replacing laggy polling integrations with AgentMail to trigger real-time agent actions via WebSockets the second a customer replies.
Stop using legacy automation platforms to route emails to your AI agents.
carousel
Sibonix is testing dedicated agent inboxes to let campaign management agents handle client revisions autonomously.
We are giving our campaign optimization agents their own corporate email addresses.
short_video_script
Programmatic provisioning of dedicated email inboxes and domains for AI agents via the Vercel Marketplace integration using a single CLI command.
We will write a deployment plan to run the 'vc i agentmail' CLI command, programmatically provision a new agent email inbox via their API, and show the live DNS routing and inbox dashboard in a step-by-step setup walkthrough.
live_workflow_walkthrough
Real-time execution of AI agent workflows via Webhooks and WebSockets immediately upon email receipt, including out-of-the-box handling of email threads and attachments.
We will build a Vercel serverless function that acts as a webhook receiver for AgentMail, passing incoming customer emails with PDF attachments to a GPT-4o agent and capturing a screen recording of the agent instantly analyzing the file and replying to the thread.
screen_recording
Instant provisioning of dedicated email inboxes and custom domains for AI agents directly via Vercel integration.
We plan to build a project deployed on Vercel, run the 'vc i agentmail' command, and demonstrate a live walkthrough of provisioning a fully functional, dedicated agent inbox (e.g., agent@ourdomain.com) within minutes without manual SMTP/DNS configurations.
live_workflow_walkthrough moderate — Requires a Vercel account, a test domain, and configuring the AgentMail integration via CLI.
Real-time AI workflow activation triggered by incoming emails using Webhooks and WebSockets.
We plan to build an automated pipeline where sending a raw customer query to the agent's new inbox instantly triggers a webhook, runs an LLM-based categorization chain, and posts the structured output to a Slack channel. We will record a screen share showing the exact latency from sending the email to the live Slack notification.
screen_recording moderate — Requires setting up a webhook receiver (using Make.com or a Next.js API route) and linking it to the AgentMail inbox.
Out-of-the-box email thread management and parsing of attachments for AI agent context.
We plan to create a before-and-after comparison showing the complex, error-prone boilerplate code needed to parse IMAP email threads and extract PDF attachments versus the clean, pre-structured JSON payload delivered natively by AgentMail.
before_after_comparison quick — Requires comparing standard Node-IMAP parsing code side-by-side with AgentMail's API response documentation.
Jul 16, 13:54 linkedin_author_expansion ai_marketing high 83.2 processed

הבעיה עם הזיכרון של סוכני AI היא לא שהם שוכחים. היא שאתם צריכים להגיד להם מה לזכור. בכל שיחה אתם מת

https://www.linkedin.com/posts/amitshafnir_%D7%94%D7%91%D7%A2%D7%99%D7%94-%D7%A2%D7%9D-%D7%94%D7%96%D7%99%D7%9B%D7%A8%D7%95%D7%9F-%D7%A9%D7%9C-%D7%A1%D7%95%D7%9B%D7%A0%D7%99-ai-%D7%94%D7%99%D7%90-%D7%9C%D7%90-%D7%A9%D7%94%D7%9D-%D7%A9%D7%95%D7%9B%D7%97%D7%99%D7%9D-activity-7481601764639641600-g1oZ?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGgyocEBLjipOn7JBXv2sz5fqM1RTEOzJOU

LangChain's OpenWiki Brains solves the tedious chore of manual AI agent memory management by autonomously building local Markdown-based wikis from sources like Gmail, Notion, and Git. For Sibonix, this local, serverless approach is a game-changer for maintaining client and project context without constant manual prompting. We should immediately test OpenWiki Brains' Personal and Code Brain modes to automate context building for our internal development and client management agents.

Read full content
הבעיה עם הזיכרון של סוכני AI היא לא שהם שוכחים. היא שאתם צריכים להגיד להם מה לזכור. בכל שיחה אתם מתקנים אותם, מזכירים להם את ההעדפות שלכם, מסבירים שוב על מה אתם עובדים. וברגע שהסשן נגמר, הכל מתאפס. ניהול הזיכרון של הסוכן הפך בעצמו לעבודה. הצוות של LangChain הוציא כלי בשם OpenWiki Brains שהופך את המשוואה. במקום שאתם תאכילו את הסוכן במה לזכור, הוא בונה לעצמו ויקי אישית עליכם. לבד, בלי שתבקשו. בפועל מחברים אותו למקורות שלכם, מ-Gmail ו-Notion ועד git, X, Hacker News וחיפוש באינטרנט. הוא רץ בזמנים קבועים על המחשב שלכם, בודק מה חדש, ומחליט בעצמו איזה מידע שווה לשמור. אתם מגדירים לו פעם אחת על מה להתמקד, נניח הפרויקטים הפעילים שלכם או לקוח מסוים, והוא כותב את ההערות בהתאם. יש שני מצבים. Personal Brain בונה ויקי מהמקורות האישיים שלכם. Code Brain יושב בתוך ריפו של git ומתעד את מבנה הקוד, ההיסטוריה והקונבנציות שלכם. הכל נשמר כקבצי Markdown פשוטים על הדיסק. קריא לבני אדם, נייד, בלי שרת באמצע והכל מקומי אצלכם. שווה לשים לב למה אתם מחברים. סוכן שקורא לכם את המייל וכותב לעצמו הערות עליכם זה נוח מאוד, אבל תהיו עירניים עם מה שאתם פותחים לו.
Ideas (7)
We are replacing manual client onboarding briefs with autonomous local wikis that feed our marketing agents real-time context.
Stop wasting hours writing briefs for your marketing AI agents.
linkedin_post
Local serverless markdown wikis allow brands to give AI agents deep context without compromising data privacy.
You do not need to upload your company's entire Google Drive to OpenAI to get personalized marketing agents.
carousel
Using autonomous local wikis to document custom marketing codebases lets dev agents ship client campaigns faster.
AI agents are terrible at remembering codebase rules until you let them write their own local documentation.
short_video_script
LangChain's OpenWiki Brains uses Personal Brain mode to autonomously build and update local Markdown wikis from personal sources like Gmail and Notion based on a one-time focus instruction.
We plan to configure OpenWiki Brains locally, pointing it to a test Gmail inbox and a Notion database containing mock client feedback. We will record a walkthrough showing the agent parsing these sources and autonomously updating a centralized 'client_profile.md' file with key project milestones and communication preferences without any manual data entry.
live_workflow_walkthrough
OpenWiki Brains features a Code Brain mode that runs inside a Git repository to autonomously document code structure, history, and developer conventions directly to local Markdown files.
We plan to run Code Brain inside a test Git repository containing a custom Python marketing agent. We will showcase a before-and-after comparison of the repository, highlighting how Code Brain automatically generates a structured 'architecture.md' file documenting the codebase structure and past commit conventions.
before_after_comparison
Autonomously building a local, serverless Markdown-based wiki (Personal Brain mode) from personal sources like Gmail, Notion, and Git to maintain up-to-date AI context.
Build a local OpenWiki Brains instance connected to a designated Google Workspace and Notion workspace containing mock client updates. We will run the agent to autonomously generate a local Markdown-based client profile wiki, then show a live walkthrough of how an AI marketing assistant uses this generated Markdown file as its system context to draft hyper-personalized client emails.
live_workflow_walkthrough moderate — Requires setting up the OpenWiki Brains repository locally, configuring Notion and Gmail API integrations, and recording the execution and resulting Markdown output.
Integrating directly with a local Git repository (Code Brain mode) to automatically analyze and document codebase structure, history, and coding conventions into Markdown files.
Deploy OpenWiki Brains in Code Brain mode over a sample Python-based marketing automation repository. We will show a before-and-after comparison of the repository's documentation directory, contrasting the initial state with the automatically generated Markdown wiki mapping the repository's endpoints, utility scripts, and developer conventions.
before_after_comparison moderate — Requires cloning a sample Python repository, running the Code Brain agent locally on the codebase, and capturing the generated documentation structure.
Jul 16, 13:54 linkedin_author_expansion ai_marketing medium 58.2 processed

Last week, I had the opportunity to share about my personal AI agents built with NanoClaw, alongside

https://www.linkedin.com/posts/zawanah_aiagents-womenintech-womendevssg-ugcPost-7474623053084143616-62vW?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGgyZFQBT5CvsekxGLqhOKNcJbM4p9Nvf8Y

This post proves that non-technical builders can successfully deploy functional multi-agent Telegram systems using NanoClaw and Claude for real-world engagement like scheduling. Sibonix should immediately look into OpenClaw and NanoClaw as lightweight frameworks for deploying client conversational agents. We need to evaluate if these specific tools can streamline our agent-building workflow without requiring heavy software engineering overhead.

Read full content
Last week, I had the opportunity to share about my personal AI agents built with NanoClaw, alongside Victoria Lo who shared about her powerful personal agent built with OpenClaw. I used NanoClaw to build a personal multi-agent system running through Telegram. I shared about my journey building the agentic system as a non-software engineer, including learning the architecture, working with Claude to build the setup I envisioned, navigating Anthropic's stance with third-party tools like OpenClaw/NanoClaw, and lessons learnt to bring forward on this continuous journey of building agentic workflows. I was inspired by Gavriel Cohen's scheduling assistant Andy which he built for AI Engineer Singapore conference earlier this year. So I built Zaria bot (with NanoClaw) where winners of our quiz get to chat with it, including booking coffee chats, and asking anything else related to our talk. Special thanks to Thein Than Khaing who thoroughly tested Zaria before going live. It has just been a few days since Zaria went live. Here's some of what she's gathered so far: 1. Users were light-handed and friendly with her - one even apologized for "wasting tokens" just to say thanks 😂 2. The most common asks were related to coffee-chat bookings, questions about Zaria herself, and slide-deck requests 3. There were more specific technical questions about OpenClaw than NanoClaw Thank you Victoria Lo for inviting me to speak with you and I appreciate that it all started with us casually sharing what we were building our agents for. The meetings and preparation for the talk was more fun than I anticipated as we shared our experiences and what we plan to explore next! 😎 Thank you to Women Devs SG for this opportunity, and the amazing volunteers for your incredible support! 💙
Ideas (2)
Non-technical builders can deploy a multi-agent Telegram assistant using NanoClaw and Claude to handle real-world scheduling workflows like coffee-chat bookings.
We will build a prototype scheduling bot using NanoClaw and Claude connected to a Google Calendar API. We will record a walkthrough showing how we structure the agent architecture with Claude's assistance, deploy it using NanoClaw, and film a live screen recording of a user successfully booking a calendar slot directly inside the Telegram chat.
live_workflow_walkthrough moderate — Requires setting up a Telegram Bot token, configuring NanoClaw, and integrating the Google Calendar API, taking about 3-4 hours of configuration and testing.
Lightweight agent frameworks like OpenClaw can run document-retrieval workflows to automatically distribute resources like presentation slide decks to chat users.
We will build an OpenClaw-based agent connected to a Telegram interface that listens for resource requests (e.g., 'send me the slides') and retrieves the correct file from a Google Drive folder. We will create a before-and-after comparison contrasting the manual effort of message-by-message follow-ups against the agent instantly serving the files in real time.
before_after_comparison moderate — Requires deploying OpenClaw, linking a Google Drive API with read permissions, and configuring the Telegram webhook, taking roughly 3 hours.
Jul 16, 13:54 linkedin_author_expansion ai_marketing high 83.2 processed

אז Sonnet 5 בחוץ! הביצועים שלו מתקרבים ל-Opus 4.8, המודל הגדול והיקר שלהם, אבל במחיר של Sonnet! בחל

https://www.linkedin.com/posts/amitshafnir_%D7%90%D7%96-sonnet-5-%D7%91%D7%97%D7%95%D7%A5-%D7%94%D7%91%D7%99%D7%A6%D7%95%D7%A2%D7%99%D7%9D-%D7%A9%D7%9C%D7%95-%D7%9E%D7%AA%D7%A7%D7%A8%D7%91%D7%99%D7%9D-%D7%9C-opus-activity-7477810241963913216-W0Bc?utm_source=combined_share_message&utm_medium=member_desktop&rcm=ACoAAGgyZFQBT5CvsekxGLqhOKNcJbM4p9Nvf8Y

Sonnet 5's ability to autonomously plan steps, run tools, and self-correct makes it an essential upgrade for Sibonix's AI agent systems. With introductory pricing of $2/$10 per million tokens until August 31st and proven capability in executing multi-step business workflows like Salesforce updates, we must immediately test it. Its lower hallucination rates and stronger resistance to prompt injections make it a highly reliable and cost-effective default for our client deployments.

Read full content
אז Sonnet 5 בחוץ! הביצועים שלו מתקרבים ל-Opus 4.8, המודל הגדול והיקר שלהם, אבל במחיר של Sonnet! בחלק מהמדדים הוא צמוד אליו ממש. המודל בנוי לעבוד לבד. הוא מתכנן צעדים, פותח דפדפן וטרמינל, מריץ כלים ומסיים משימות מורכבות. שותפים בגישה מוקדמת מספרים שהוא מסיים עבודות בדיוק בנקודות שבהן דורות קודמים של Sonnet היו נעצרים באמצע, ושהוא בודק את התוצאות של עצמו בלי שמבקשים ממנו. מהנדס Rust סיפר שהמודל כתב מיוזמתו טסט שמשחזר את הבאג, יישם את התיקון, ואז העלים אותו לרגע כדי לוודא שהבאג אכן חוזר, הכל במעבר אחד. בתהליכים עסקיים הוא מעדכן דרגות לקוח ב-Salesforce ושולח הודעות השקה ברצף אחד, בדיוק במקומות שבהם דורות קודמים שלו היו נתקעים באמצע. ויש מי שכבר מריץ איתו תהליכי ביטוח שלמים ומחקר משפטי מורכב מקצה לקצה. במקביל שיעורי ההזיה ירדו והוא הרבה יותר עמיד מול תקיפות של Prompt Injection. מהיום הוא ברירת המחדל בכל חשבונות ה-Free וה-Pro, כך שכל מי שנכנס לקלוד היום כבר עובד איתו בלי לעשות כלום. הוא זמין גם ב-Max, ב-Team, ב-Enterprise, ב-Claude Code ובפלטפורמה. מבחינת מחיר, עד 31 באוגוסט יש מחיר היכרות של 2$ למיליון טוקנים נכנסים ו-10$ למיליון טוקנים יוצאים, ואחרי אוגוסט 3$ ו-15$. יחד עם כל זה, הייתי רוצה להגיד שאני מתרגש, אבל אין על אופוס (אחרי פאבל) 😊
Ideas (7)
Sibonix is migrating client marketing agent workflows to Sonnet 5 immediately to capitalize on the $2 per million input token pricing before the August 31st deadline.
We are migrating our client AI agent systems to Sonnet 5 this week.
linkedin_post
Sonnet 5's ability to self-correct and update tools like Salesforce without getting stuck makes it the new default engine for our autonomous marketing pipelines.
AI agents that fix their own mistakes are officially here.
carousel
We are stress-testing Sonnet 5's prompt injection resistance to ensure our client-facing deployment systems remain secure at scale.
Security is the biggest bottleneck in scaling brand AI agents.
short_video_script
Sonnet 5 can autonomously execute multi-step business workflows, such as updating customer tiers in Salesforce and sending launch messages in a single, uninterrupted sequence.
We will build an automation flow connecting Sonnet 5 to a Salesforce sandbox and a Slack workspace. We will film a walkthrough showing the agent receiving a customer upgrade trigger, updating the account tier in Salesforce, and drafting and sending the corresponding announcement message in Slack without any human intervention.
live_workflow_walkthrough
Sonnet 5 features autonomous self-correction capabilities, enabling it to write tests to reproduce bugs, apply fixes, and verify the final outcomes independently.
We will set up a codebase containing a specific, pre-determined bug and task Sonnet 5 with resolving it. We will record a screen share of the terminal showing the model writing a test to isolate the bug, applying the code fix, and running the test suite to confirm the error is fully resolved.
screen_recording
Sonnet 5's autonomous self-correction capability allows it to write tests to reproduce bugs, apply fixes, and run verification steps entirely on its own in a single pass.
We will build a local development sandbox containing a broken Python data-processing script with a hidden edge-case bug. We plan to record a screen capture of Sonnet 5 using a terminal tool to write a failing unit test, modify the script to fix the bug, and re-run the test to verify execution success without human intervention.
screen_recording moderate — Requires setting up a sandboxed execution environment, writing a buggy sample script, and configuring the agent's tool-use permissions (approx. 3-4 hours).
Sonnet 5 can execute complex, multi-step business workflows—such as updating customer tiers in Salesforce and sending launch notifications—without getting stuck midway.
We plan to build a multi-step agent workflow connected to a Salesforce sandbox API and a Slack notification channel. We will run a side-by-side comparison showing how a previous generation model stalls during the API handoff versus Sonnet 5 autonomously updating the lead status and drafting/dispatching the launch notification in a single run.
before_after_comparison substantial — Requires setting up a Salesforce developer sandbox, API integrations, Slack webhook configuration, and running multiple test iterations to capture comparative failure/success states (approx. 1-2 days).
Jul 16, 09:44 telegram_yoav ai_marketing medium 72.8 processed

https://www.linkedin.com/posts/amitshafnir_%D7%9E%D7%99%D7%A9%D7%94%D7%95-%D7%A0%D7%AA%D7%9F-%D7%9C%

https://www.linkedin.com/posts/amitshafnir_%D7%9E%D7%99%D7%A9%D7%94%D7%95-%D7%A0%D7%AA%D7%9F-%D7%9C%D7%9E%D7%95%D7%93%D7%9C-%D7%92%D7%99%D7%A9%D7%94-%D7%9C%D7%92%D7%9C%D7%A8%D7%99%D7%99%D7%AA-%D7%94%D7%AA%D7%9E%D7%95%D7%A0%D7%95%D7%AA-%D7%A9%D7%9C%D7%95-ugcPost-7482669937191399425-AHkQ/?utm_source=social_share_send&utm_medium=member_desktop_web&rcm=ACoAABv4824By_zzh9Qo4y3K-_QmjljOCRHqPz4

This use case proves that personal photo galleries can be treated as structured databases for deep profiling and personalized asset generation. Sibonix should investigate the creator Thijs's released extraction skill to understand how we can leverage image-gallery scanning for client profiling. We must weigh the massive personalization potential against the obvious privacy implications of granting agents full gallery access.

Read full content
מישהו נתן למודל גישה לגלריית התמונות שלו, וביקש ממנו לחלץ כל פריט לבוש שהוא מחזיק בארון. הוא קיבל קטלוג מלא של הבגדים שלו, מחולץ מתוך תמונות רגילות שצילם לאורך השנים. חולצות, מכנסיים, ג׳קטים, נעליים, כל אחד בקטגוריה משלו. הוא ביקש מהמודל להרכיב לו לוקים חדשים מהבגדים שכבר יש לו, ואז לרנדר את הלוקים האלה עליו עם מודל תמונות. מה שיצא זה גלריה של אאוטפיטים, כולם עם הפנים שלו, כולם מבגדים שהוא כבר קנה. לבחור שבנה את זה קוראים Thijs, והוא עשה את זה עם קודקס. הוא גם שחרר את הסקיל שעושה את החילוץ. נותנים לו תיקייה של תמונות, או פשוט אומרים לו לסרוק את הגלריה, והוא מתחיל לעבוד. צריך לקחת בחשבון שזה יכול לקחת הרבה זמן. מה שהזוי זה שהגלריה שלנו היא מאגר נתונים שאפשר לנתח אותו. בתוך אלפי התמונות האלה יש מידע על מה שיש לנו בבית, מה אנחנו קונים, איפה אנחנו מבלים, ומי נמצא לידנו. עד עכשיו לא הייתה דרך פרקטית לחלץ את זה. עכשיו יש. אותה יכולת שבונה לנו ארון דיגיטלי יכולה גם לבנות פרופיל מדויק עלינו. שווה לחשוב על זה לפני שנותנים לסוכן גישה מלאה לגלריה. מצד שני, זה יכול להיות סופר מעניין לקבל זווית חדשה, דרך המודל, לכל מטרה שהיא. להבין איפה אתם אוהבים לבלות, מה אתם לובשים, איזה רגעים בחיים שלכם ״שווים״ תמונה, וכו׳. סם אלטמן הבטיח מתנה מהארכיון של OpenAI למי שיבנה את הדבר הכי מגניב עם המודל החדש. נראה לי שהוא מצא אותו.
Ideas (2)
Extracting structured asset inventories and product catalogs directly from raw, unstructured lifestyle photo galleries using vision models.
We will build a workflow using Make.com, GPT-4o, and Airtable that ingests a folder of raw lifestyle images, automatically detects and crops individual fashion/product items, categorizes them (e.g., shirts, shoes, accessories), and populates a structured database with tags. We will show a before/after comparison of the chaotic raw image folder versus the cleanly organized, searchable Airtable catalog.
before_after_comparison moderate — Requires setting up a Make.com scenario connecting Google Drive, OpenAI's GPT-4o API, and Airtable, taking about 3 hours to configure and test.
Generating highly personalized visual marketing assets by mapping extracted inventory items back onto a user's likeness using virtual try-on models.
We will design a pipeline using Replicate's API (utilizing a virtual try-on model like IDM-VTON) to take a portrait photo of a user and overlay an extracted clothing item from their catalog, rendering a realistic new lifestyle image. We will record a screen walkthrough showing the API call, the input parameters (user photo + clothing item), and the final generated output of the user 'wearing' the item.
screen_recording substantial — Requires setting up a custom script or workflow utilizing specialized diffusion-based try-on models on Replicate, taking 1 to 2 days of testing to ensure clean visual outputs.
Jul 16, 09:44 telegram_yoav seo medium 72.8 processed

https://www.linkedin.com/posts/andrey-soloviev_the-ai-search-seo-guide-to-claude-is-here-share-74831

https://www.linkedin.com/posts/andrey-soloviev_the-ai-search-seo-guide-to-claude-is-here-share-7483114345309143040-fR9A/?utm_source=social_share_send&utm_medium=member_desktop_web&rcm=ACoAABv4824By_zzh9Qo4y3K-_QmjljOCRHqPz4

The provided content is too thin to extract concrete insights as it only contains a URL pointing to a guide about Claude and AI search. Sibonix cannot act on this immediately without manually retrieving and reviewing the actual LinkedIn post and linked guide to evaluate its specific optimization tactics.

Read full content
https://www.linkedin.com/posts/andrey-soloviev_the-ai-search-seo-guide-to-claude-is-here-share-7483114345309143040-fR9A/?utm_source=social_share_send&utm_medium=member_desktop_web&rcm=ACoAABv4824By_zzh9Qo4y3K-_QmjljOCRHqPz4
Ideas (2)
Optimizing web content structure specifically to trigger citations and recommendations in Claude's search-enabled LLM responses.
We will build two versions of an informational service page—one optimized with traditional keyword SEO, and the other structured with Claude-friendly formatting (direct markdown headers, XML-style concept blocks, and explicit Q&A schemas). We will then run live API queries using Claude 3.5 Sonnet with web search enabled to compare which page version is prioritized for citations and show the comparison of retrieval success.
before_after_comparison moderate — Requires deploying two test URLs, ensuring they are indexed by search engines, and running comparative API queries to document Claude's citation behavior.
Measuring brand visibility and 'Share of Voice' within Claude's search engine integrations relative to competitors.
We will build a Python automation script that runs a batch of 50 industry-specific search queries through Claude's web-search-enabled model, parses the citations, and generates a visual dashboard showing which brands dominate Claude's recommendations. We will record a walkthrough of the script running and the resulting dashboard to prove how brands can audit their AI search footprint.
live_workflow_walkthrough substantial — Requires writing a custom Python parsing script, integrating with an LLM API that supports web search, and building a simple Streamlit dashboard.
Jul 16, 09:44 telegram_yoav ai_marketing medium 72.8 processed

https://www.instagram.com/reel/DaDTcINOr8l/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==

https://www.instagram.com/reel/DaDTcINOr8l/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==

This workflow of combining Seedance 2, Atoms.dev, and Claude to turn static photos into interactive 3D websites is a highly compelling creative use case for real estate and high-end brand landing pages. Sibonix should test this exact tool stack—specifically using Claude to bridge Seedance's video outputs with Atoms.dev's interactive web capabilities—to elevate our web design deliverables. The high comment engagement proves there is massive market interest in this specific visual output.

Read full content
Comment AI for the link to turn ordinary apartment photos into cinematic, scrollable real estate websites with AI. Learn how I used Seedance 2 to create luxury fly-through videos from static images, then used Atoms.dev and Claude to turn them into a fully interactive website experience with 3D scroll effects, mouse tracking, and immersive visuals. I’ll send you both prompts. #aitools #atomsdev #atoms #aiwebsites #seedance2 Top comments: - Ai - Ai - AI - Ai - AI
Ideas (2)
Generating cinematic luxury fly-through videos from static real estate images using Seedance 2.
We will take a single, high-quality static photo of a modern luxury living room, feed it into Seedance 2, and apply camera-path prompts to generate a 4-second cinematic fly-through video. We will display the original static photo side-by-side with the rendered video output to showcase the depth and motion quality generated from a single image.
before_after_comparison moderate — Requires setting up a Seedance 2 account, generating several camera-path variations, and editing the side-by-side comparison video.
Using Atoms.dev and Claude to convert video outputs into an interactive 3D scroll-mapped website experience with mouse tracking.
We will build a functional prototype of a luxury landing page using Atoms.dev. We will use Claude to generate the custom JavaScript that maps the Seedance-generated fly-through video's playback to the user's scroll position and implements a subtle mouse-tracking 3D tilt effect on the hero section, capturing the entire build process and the final live interaction.
live_workflow_walkthrough substantial — Requires accounts on Atoms.dev and Claude, writing and debugging scroll-bound video playback code, and recording a live screen walkthrough of the interactive site.
Jul 16, 09:44 telegram_yoav ai_marketing medium 72.8 processed

https://www.instagram.com/p/Daz7gnPgtwI/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==

https://www.instagram.com/p/Daz7gnPgtwI/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==

This post highlights ChatGPT's evolution into a platform capable of building websites and tools directly using features like 'Work' and 'Skills'. Sibonix should investigate these specific ChatGPT updates to see if they can streamline our AI agent building process. Additionally, the comment-to-DM lead generation tactic ('Work') demonstrated here is a highly effective organic social mechanism we should replicate.

Read full content
אתם עדיין נכנסים ל ChatGPT כדי לשאול שאלה, לנסח הודעה או לייצר תמונה ויוצאים? בשקט בשקט צ׳אט ג׳פט הפך למשהו הרבה יותר גדול. המרחק בין רעיון שיש לכם בראש, לבין משהו אמיתי שאפשר להשתמש בו, הפך להיות קצר בצורה מטורפת. בקרוסלה הזאת עשיתי סדר במה באמת השתנה, מה זה Work, איך מחברים מידע, מה אפשר לעשות עם Skills ואיך כל זה מתחבר גם לבניית אתרים וכלים ישירות מתוך ChatGPT. רוצים את המדריך המלא? כתבו לי Work בתגובות ואשלח לכם אותו ישירות. Top comments: - Work - Work - Work 👍🏼 - Work - Work
Ideas (2)
Building functional web tools and interactive interfaces directly inside ChatGPT's workspace using features like Canvas and code execution.
We will build a custom interactive 'Lead Score Calculator' tool entirely within ChatGPT's Canvas workspace, showing the exact prompting sequence used to generate the functional HTML/JS, and then load the finished code into a live browser to show it calculating scores in real time.
live_workflow_walkthrough moderate — Requires drafting the prompt sequence in ChatGPT Plus to generate clean code and deploying the raw code to a quick staging environment like Netlify or CodePen for the live demonstration.
The comment-to-DM organic lead generation mechanism ('Comment Work to get the guide') to automate resource delivery and boost social engagement.
We will set up a live ManyChat automation flow triggered by the keyword 'Work' on an Instagram post, showing a split-screen screen recording of the backend configuration workflow alongside a real-time mobile screen recording of a user commenting and instantly receiving the automated DM.
screen_recording quick — Requires an active Instagram Professional account connected to a ManyChat free/pro plan to configure the single-keyword automation rule and test it with a dummy account.
Jul 16, 09:44 telegram_yoav creative medium 72.8 processed

https://www.instagram.com/reel/DaxBBOYgqxl/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==

https://www.instagram.com/reel/DaxBBOYgqxl/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==

This post is a lead-generation hook offering a guide on combining an AI tool with Luma's new 'skills' feature to maintain aesthetic realism. Because the actual prompt technique is gated behind a comment-to-DM automation, the content itself is too thin to extract the exact method. Sibonix should immediately investigate Luma Labs' new 'skills' feature to see how it can be integrated into our creative workflow for high-end ad generation.

Read full content
סידנס 2 האו כלי מטורף, אבל! הוא יכול לעלות לכם הון ולא לתת לכם את התוצאות הרצויות. אז עליתי על טריק שמאפשר לכם להכווין אותו כמו שצריך, מבלי להשתלט לו על הריאליזם והאסתטיקה היפה שהוא יכול להוציא. ובשילוב הסקילים החדשים בלומה? בכלללל החיים שלנו השתנו 🤩 תגיבו לסרטון ואשלח לכם מדריך מלא כולל פרומפטים לבניית הסקילים 👇🏾 @lumalabsai Top comments: - מדריך - מטורף!!! - אשמח - אלוף - אשמח❤️
Ideas (2)
Luma Labs' 'Skills' feature allows creators to define custom instructions and presets to steer camera movements and styles in video generation while maintaining aesthetic realism.
We will build a custom 'Macro Cinematic' Skill in Luma Dream Machine designed for high-end product shots. We will run identical prompts—one using the default Luma generation and one using our custom Skill—and display them side-by-side to show how the custom Skill enforces precise camera control without losing visual fidelity.
before_after_comparison moderate — Requires access to Luma Dream Machine's custom configuration tools, drafting specific steering prompts, and rendering several comparison video pairs.
Combining a high-fidelity image generator with Luma's 'Skills' feature to animate static assets into realistic product commercials.
We will design a workflow where we generate a static, hyper-realistic luxury cosmetic bottle in Midjourney, then import it into Luma Dream Machine. We will apply a custom-built '360-degree orbital sweep' Skill to animate the bottle, and walk through the exact prompt logic and settings required to get a seamless, production-ready video ad.
live_workflow_walkthrough moderate — Requires active Midjourney and Luma Dream Machine accounts, setting up the image-to-video pipeline, and recording the screen during the configuration.
Jul 16, 09:44 telegram_yoav ai_marketing high 97.8 processed

https://www.instagram.com/reel/Da0Mb0xOtuP/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==

https://www.instagram.com/reel/Da0Mb0xOtuP/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==

Connecting Runway directly to Claude via the MCP connector is a highly actionable workflow upgrade for generating ad videos straight from chat. Sibonix should immediately set up this custom connector using the provided Runway MCP URL to streamline creative asset generation. This brings video production directly into the Claude interface, eliminating tool-switching friction.

Read full content
Comment “MCP” and we’ll send you the full guide to Runway MCP Connect Runway to Claude in seconds and start generating straight from chat. 1. Go to Claude → Customize → Connectors 2. Add a custom connector, name it Runway and paste: https://mcp.runwayml.com/mcp 3. Click Add → Connect and sign in with your Runway account 4. Ask Claude to make you an ad video Your creative tools, one prompt away. Top comments: - MCP - MCP - MCP - Mcp - MCP
Ideas (7)
Integrating the new Runway MCP into Claude is the first step toward building fully autonomous video ad production pipelines for growth brands.
You can now generate Runway videos inside Claude without leaving the chat.
linkedin_post
Direct chat-to-video tools like the Runway MCP only work if you feed them highly structured brand DNA and pre-validated script templates.
Generating ads directly inside Claude will waste your budget if you do not feed the AI a pre-validated performance library first.
carousel
We are building multi-agent systems that use the Runway MCP to automatically generate, edit, and queue variations of social media ads without manual prompting.
We are connecting the new Runway MCP to multi-agent systems to automate the entire creative testing loop.
short_video_script
Connecting Runway directly to Claude via the Model Context Protocol (MCP) connector using a custom URL to trigger video generation straight from the chat interface.
We will configure the custom Runway connector inside the Claude interface using the official Runway MCP URL, and film a live walkthrough showing the step-by-step setup followed by a successful connection test.
live_workflow_walkthrough
Generating ad videos directly within Claude using natural language prompts via the integrated Runway MCP tool without switching interfaces.
We will draft a specific prompt for a product video ad, submit it directly to Claude with the Runway connector active, and show a before-and-after comparison of the raw text prompt alongside the final rendered ad video generated inside the chat.
before_after_comparison
Connecting Runway to Claude as a custom Model Context Protocol (MCP) connector to bring video generation directly into the chat interface.
We will record a live workflow walkthrough configuring the custom Runway MCP connector inside Claude's settings, pasting the official Runway MCP URL, and completing the authentication handshake to show the active connection.
live_workflow_walkthrough moderate — Requires access to Claude's custom connector feature, a Runway account, and about 1-2 hours to configure and record the setup process.
Generating promotional ad videos directly inside the Claude chat interface using natural language prompts via the connected Runway tool.
We will write a prompt in Claude for a 5-second cinematic product ad for a mock CPG brand, trigger the Runway MCP tool, and capture a screen recording of the video generating and rendering directly within the chat window.
screen_recording moderate — Requires an active Runway API/connector subscription and 1 hour to test prompts and record the real-time generation sequence.
Jul 16, 09:44 telegram_yoav ai_marketing high 97.8 processed

https://www.instagram.com/p/DaydOp3jdX0/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==

https://www.instagram.com/p/DaydOp3jdX0/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==

This five-role Claude orchestration is a highly practical blueprint that Sibonix should implement immediately to scale content production. We must replicate this exact pipeline—from Strategist to Editor—inside a shared Claude Project to eliminate creative bottlenecks. The concrete next step is to write and test the specific system prompts for these five distinct personas to run an end-to-end content generation test.

Read full content
⬇️ Build an entire content team with Claude If you’re still thinking of Claude as one assistant doing one task, it’s time to wake up. Brands are now using it to run a full content team, with each role handled by Claude, working inside the same project so nothing gets lost between steps. → Person 1 is the Content Strategist. It pulls current data, trends and brand performance to identify what to create next, then turns it into a content concept ready to hand off. → Person 2 is the Creative Director. It takes that concept and builds the visual direction around it, referencing your brand guidelines so everything stays on aesthetic and on brand. → Person 3 is the Copywriter. It receives the concept and direction and writes everything the post needs, from captions to hooks to copy, written to match your brand voice. → Person 4 is the AI Director. It takes the creative concept and writes the prompts for every image and video, sends them to the right AI models, manages the generation and saves the finished assets into the correct folders, named and organised. → Person 5 is the Editor. It picks up the generated assets and written copy and assembles the final post, adding captions, cuts and sequencing, then outputs a finished, publish-ready piece of content for you to review. The point is that this isn’t one tool doing a task. It’s a full team, orchestrated end to end, where every role knows what the others have done. That’s what makes it possible to produce content at scale without the usual bottlenecks. ➡️ Comment "scale" to learn how to use Claude for your brand. Follow @mobileeditingclub for more editing and AI tips and news. #ai #claude #aiagent #aicontent #claudeai Top comments: - Scale - Scale - Scale - Scale - Scale
Ideas (7)
We are actively deploying the five-agent Claude pipeline to run content operations for our clients, moving past single-prompt generation into automated team orchestration.
Stop asking Claude to write your posts and start building a five-agent editorial board inside your workspace.
linkedin_post
The success of a multi-agent Claude team depends entirely on the handoff protocols between the strategist, copywriter, and editor agents.
We built the five-role Claude content system, here are the exact prompt handoffs that keep the agents from breaking.
carousel
The transition from solo AI prompting to multi-agent orchestration is the single biggest shift in scaling brand content this year.
Your competitors are not writing better prompts, they are building multi-agent networks that talk to each other.
short_video_script
Multi-persona orchestration inside a single Claude Project to pass a content concept sequentially from a Strategist to a Creative Director and a Copywriter.
We will build a Claude Project configured with custom system instructions for these three specific personas. We will record a live workflow walkthrough showing a raw trend URL being processed sequentially by each persona in the chat history, resulting in a finalized, on-brand social copy draft and visual brief.
live_workflow_walkthrough
An AI Director persona that translates a creative concept into structured, syntax-perfect prompts optimized for specific image and video generation models.
We will build a specialized system prompt for the AI Director persona and run a test where it takes a raw text concept and outputs optimized prompts for Midjourney v6 and Runway Gen-3. We will present a before-and-after comparison showing the raw creative concept alongside the highly detailed generated prompts and the actual visual assets produced by running those prompts.
before_after_comparison
Multi-persona orchestration inside a single Claude Project where specialized roles (Strategist, Creative Director, Copywriter, Editor) sequentially build on each other's work using shared project memory.
We will build a Claude Project pre-loaded with custom system instructions for all five personas (Strategist, Creative Director, Copywriter, AI Director, and Editor). We plan to run a live test starting with a raw trend input and record a step-by-step walkthrough showing how each persona inherits and refines the previous agent's output to produce a ready-to-publish asset.
live_workflow_walkthrough moderate — Requires drafting five distinct system prompts, configuring them inside a Claude Project, and executing a complete run-through to capture the screen recording.
An automated 'AI Director' agent that translates creative concepts into structured image/video prompts, triggers external generation models, and organizes the resulting assets.
We will build a Make.com integration that connects Claude to a Midjourney API (like GoAPI) and Google Drive. The plan is to feed the scenario a creative brief, watch the AI Director agent generate the exact prompt parameters, trigger the generation, and automatically save the final high-res images into a neatly named Google Drive folder.
screen_recording substantial — Requires setting up a Make.com workflow, integrating third-party AI image APIs, configuring Google Drive folder creation logic, and running end-to-end integration tests.