18 posts found
Jensen Huang said AGI has arrived. OpenAI's own president hinted at the same thing. But the benchmark propping up that celebration hides an uncomfortable fine print. I explain why the engineering truth behind Astra is far less magical than the pitch.
OpenAI shipped GPT-6 Astra, the first model built to work alone for days at a time. I explain why this launch feels different from everything that came before — and where the marketing narrative already oversteps.
A white paper bringing together researchers from Google, Stanford, Harvard, and Princeton proposes something that caught me off guard: maybe AGI won't come from text, but from video models like Veo 3. I explain why that hypothesis isn't as crazy as it sounds.
Your RAG works great in testing and falls apart in production? I've been there. The problem is almost never the LLM — it's the retrieval pipeline. Here are the 4 steps I use to fix it for good.
Every time Claude goes down, someone makes a joke on X. I think the joke is hiding a serious architecture decision: is it worth depending on an intelligence that occasionally leaves you stranded?
Meta named the problem: Behavioral State Decay. The instruction is there. The AI 'sees' it. But stops acting on it. The fix: a second AI agent — the 'shoulder tap' — that injects selective reminders. +8.3 points on Terminal-Bench. Without retraining the model.
#1 on MTEB English by 5.09 points. 5 modalities in a single 3,072-dimensional vector. Matryoshka to truncate to 768 without retraining. Text, images, 128s video, 80s audio, 6-page PDFs. Google solved the problem that forced teams to maintain 3 models and 3 indexes.
86.7% overlap between Claude's citations and Brave Search results. ReAct loop with up to 20 iterations. Dynamic filtering via Python code. Cost of $0.08-$0.25 per searched response. The anatomy of how an AI decides to verify facts instead of inventing them.
GPT-4o: from 91% with 5 words to 15% with 40. In mixed conditions: 1%. GPT-5, Claude Opus 4.1 and Gemini 2.5 also failed. A PNAS Nexus paper proved: transformers have no 'referee' for resolving attention conflicts. It's an architectural limitation.
A compiled 3B model beat the same 3B model with external orchestration on 4 of 5 metrics (p<0.001). 128-462x cheaper. 2.8x faster. 87-98% quality. The paper challenging LangGraph, CrewAI, and the entire orchestration industry.
Cursor 3 launched multi-agent with 8 parallel agents. Claude Code fits 30,000 lines in a single prompt. Diffs fail 40% of the time — full rewrites drop it to 0%. Here's the architecture that makes it all work.
Gartner predicts 40%+ of agentic AI projects will be scrapped by 2027. Anthropic says: 'don't build agents when workflows suffice.' And in 2026, the market conversation shifted from 'building agents' to 'orchestrating agents.' Start with the rails.
Prompt injection isn't a bug — it's a fundamental property of current LLM architecture. The Countermind paper proposes 4-layer defense. OWASP's Top 10 for LLMs lists prompt injection as vulnerability #1. And your 'hardened' system prompt won't save you.
FloTorch's February 2026 benchmark shocked the community: recursive 512-token chunking BEAT semantic. 80% of RAG failures trace to ingestion, not the LLM. And the 'simple' strategy is often the best. Here's the guide I wish I'd read sooner.
+8.3% accuracy. Up to 2.4x faster. 75.6% fewer tokens. Training cost: $4.27. RecursiveMAS proves that when AIs stop 'talking' and start 'thinking together,' everything changes.
60% of production projects use both. The combined approach hits 96% accuracy vs 89% RAG-only and 91% fine-tuning-only. Salesforce's AI VP cut hallucinations from 12% to 3%. But where do you start?
512,000 lines of code leaked. 1,906 files. And the revelation that shocked the community: the AI itself is a while loop with one core function. The other 98% is traditional software engineering. Brilliant.
Did Claude Skills come to replace MCP? No. They're complementary layers of the same architecture. Here's why using both together is the right answer.