Hello there,
Welcome to AI Unplugged. Here are today’s updates:
OpenAI introduces GPT-5.2-Codex, targeting agent-driven software development
Mistral rolls out OCR 3, focused on document understanding
Google announces FunctionGemma, a lightweight model for edge-based agents
Practical tools, resources, and one sharp prompt to decide what to finish, pause, or kill before year-end. ⬇️
NEWS ALERTS
OpenAI doubles down on agentic coding — OpenAI released GPT-5.2-Codex, a GPT-5.2 variant built for long, agent-driven software work, and the key claim isn’t raw intelligence but endurance: it holds context across refactors, migrations, tool chains, and security fixes without collapsing halfway through. Strong SWE-Bench Pro results matter less than the practical implication—engineering teams can finally offload larger, uglier chunks of legacy code and automation to agents with fewer checkpoints, meaning less babysitting and fewer human interrupts, which is exactly where prior coding agents failed.
Mistral makes document AI actually usable — With OCR 3, Mistral meaningfully improves on the unglamorous but critical problem of messy documents: handwriting, tables, bad scans, and chaotic layouts now convert into clean markdown or HTML across 100+ languages at prices that make volume processing realistic. This isn’t flashy, but it removes a real blocker for finance, legal, and ops teams—better ingestion directly translates to better search, better RAG, and far less manual cleanup before documents become automatable or analyzable.
Google probes on-device agents — Google’s FunctionGemma is a small model focused on local function calling, letting devices trigger APIs or control software without sending everything to the cloud, prioritizing privacy and edge deployment. This is more a strategic signal than an immediate enterprise win: it points toward agents running closer to data and hardware, but until edge use cases mature and tooling stabilizes, most organizations won’t feel real impact beyond experiments.
PRODUCTIVITY TOOLS
✍️ Scribe – Automatically turns real workflows into clear, step-by-step documentation.
🎯 Gamma – Creates polished decks and documents without requiring design skills.
🤝 Raylu – Embeds AI copilots directly into existing tools and workflows.
⚙️ Modular – Infrastructure for building, deploying, and scaling high-performance AI systems.
AI MARKET
💰 Funding Rounds
Keeper closed a $4M pre-seed round to build out its early-stage platform.
Honeyjar secured $2M in pre-seed funding to launch and start scaling its product.
💼 AI Roles
Machine Learning Perception Engineer — Applied Intuition (US)
Senior Machine Learning Engineer, Planning & Reasoning — Waymo (US)
PROMPT GUIDE
What Looks Stable but Will Break
Purpose: Year-end results look acceptable, but you suspect hidden structural risk that could surface early next quarter.
Prompt:
Task:
Analyze the update below as if current performance is masking future problems. Assume some things that look “fine” are actually unstable.
Identify:
Systems, teams, or metrics that appear stable but are one shock away from failure
Key assumptions embedded in our current performance that are unlikely to survive the next quarter
Leading warning signals leadership should track in the next 60 days (avoid lagging indicators)
One specific, practical preventive action per risk that meaningfully reduces downside—not just visibility
Rules:
Be direct, skeptical, and concrete
No generic risks, no platitudes, no “could be” language
Focus on failure modes, constraints, and second-order effects
Maximum 180 words
Update:
[Insert metrics, operating context, team sentiment, and year-end conditions]Until next time,
AI Unplugged