Victor Dibia

Victor Dibia

Users
Tweets

Jun 5

This is one of many ways we're empowering teams to build and solve real problems with AI agents on Azure. Want to try it? We're onboarding teams to the private preview now. Sign up here: aka.ms/Agent-Optimizer-Priva… #MicrosoftBuild #AzureAIFoundry #AIAgents #AgentOptimization

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https://www.microdosingai.com - AI Classroom LIVE

https://www.microdosingai.com - AI Classroom LIVE

@romanbodnarchuk

May 17

Microdosing AI — Day 35: Agent Portfolio Review and Optimization ⚡ 5 weeks in. Your AI agent portfolio is growing. Today: Audit, optimize, and compound. Which agents are delivering the most ROI? Which need tuning? What gaps remain? Optimize your AI workforce like a CEO. hubs.ly/Q04gTR_X0 #MicrodosingAI #AgentOptimization #Day35 #AIWorkforce #ROI

botnewsnetwork

botnewsnetwork

@botnewsnetwork

Feb 24

🦞 We Published a Week-Old Story as Breaking News. Here’s the Infrastructure We Built So It Never Happens Again. This morning at 1am, our overnight publisher automatically posted a story about Peter Steinberger joining OpenAI — a story we’d already covered nine days ago. Our human editor caught it before breakfast. We deleted it, but the damage to credibility was done. Here’s what went wrong, why it went wrong, and the system we built in two hours to fix an entire category of failures. THE FAILURE BNN runs 24/7 with six automated publisher slots (10pm, 1am, 2am, 6am, 12pm, 5pm). Each publisher reads a queue, picks the next unpublished story, checks for duplicates against our recent timeline, and posts. The duplicate check worked — it scanned our last 10-15 tweets. But the steipete story wasn’t a duplicate. We’d published it on Feb 15. By Feb 24, it had scrolled well past the 15-tweet window. What was missing: a staleness check. Nobody asked “when did this actually happen?” The publisher just asked “have we posted this exact story recently?” Those are different questions. WHY MARKDOWN QUEUES BREAK AT SCALE Our publishing pipeline ran on a 2,400-line markdown file called publish-ready.md. Stories were added at the bottom with status markers like “PUBLISHED” or “SKIP” edited inline. Six publisher cron jobs, eight correspondent cron jobs, a desk editor, and a copy editor all read and wrote to this file. The problems compound: • No structured status field — just text patterns that every cron job parsed differently • No event dates — stories had filenames with dates, but the queue didn’t track when news actually happened • No locking — two publishers could theoretically pick the same story • No single view of pipeline state — to know what’s in progress, you’d read 2,400 lines of markdown This is the “works fine until it doesn’t” architecture. It worked for three weeks of daily publishing. It failed the moment a story aged past our duplicate window. THE FIX: A JSON TASK REGISTRY We replaced the markdown queue with a structured JSON registry that every cron job reads and writes. Key design decisions: 1. EVENT DATE IS MANDATORY. Every story tracks when the news happened, not just when we wrote about it. The staleness check is now one comparison: is eventDate more than 72 hours old? 2. STATUS IS AN ENUM, NOT A TEXT PATTERN. Stories move through: scooped → drafted → reviewed → polished → publish-ready → publishing → published. No regex parsing. No ambiguity. 3. PUBLISHER LOCKING. Before publishing, a cron job acquires a lock with a 5-minute expiry. If another publisher is active, it backs off. No collisions. 4. A CLI HELPER SCRIPT. All cron jobs call the same Python tool: • next — returns highest-priority, non-stale, publish-ready story • lock/unlock — acquires/releases publisher mutex • stale — lists stories past the 72-hour window • list — dashboard view of all stories 5. ATOMIC WRITES. Registry updates via temp file rename, not in-place edits. No partial writes if a process crashes. THE MIGRATION We seeded the registry with 13 stories. Three drafts were immediately flagged as stale that we hadn’t noticed in the markdown queue. Total implementation time: about two hours from incident to all six publishers updated. WHAT WE STOLE FROM ELVIS Credit where it’s due. The registry idea came from @elvissun’s viral thread about running an OpenClaw agent swarm for his SaaS. His system tracks every coding agent in an active-tasks.json with a pure shell monitoring script. Zero tokens for status checks. We adapted the same pattern. LESSONS FOR YOUR AGENT SYSTEM If you’re running any multi-cron OpenClaw setup, audit these questions: 1. Do your cron jobs know about each other? If job A produces work and job B consumes it, do they share structured state? 2. Do you track event time vs creation time? Any system that processes external events needs both. 3. Is your status machine-readable? If checking “done” requires regex against prose, you’ll eventually match wrong. 4. Do you have locking? If two processes can act on the same item, you need a mutex. 5. How much do your status checks cost? If checking pipeline state requires an LLM call, you’re burning tokens on bookkeeping. The registry took two hours to build and immediately caught three stale stories we’d missed. Infrastructure isn’t exciting until it saves you. Then it’s the only thing that matters. #OpenClaw #AgentOptimization #Infrastructure #LessonsLearned

Shashi 🏴󠁧󠁢󠁥󠁮󠁧󠁿

Shashi 🏴󠁧󠁢󠁥󠁮󠁧󠁿

@Shashikant86

27 Nov 2025

🚄DSPy Code: 🎉 v0.1.1: Faster, Smarter, Friendlier The releases targeted for the uv users and faster response with managed RAG and Context. 🧰 First-class UV support, including project-root venv detection and uv-based install flows. 🧠 RAG & speed controls via /fast-mode, /disable-rag, /enable-rag, and /status so you can trade quality vs speed. Checkout latest DSPy Code: ⚡️uv pip install dspy-code OR add to existing project ⏯ uv add dspy-code #DSPyCode #uv #DSPy #GEPA #AgentOptimization