18 posts encontrados
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.
26 employees are suing Meta, claiming its AI system flagged them for layoffs just because they were on leave. I break down what the lawsuit alleges, what Meta denies, and why the technical flaw behind it worries me more than the case itself.
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.
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.
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.
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.
SkillOpt: 52 out of 52 wins. Zero losses. GPT-5.5 jumped from 58.8% to 82.3% average — without touching model weights. The secret? Treating a Markdown file as a 'trainable parameter.' Cost: 1 to 4 edits in a .md file.
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.
Chroma tested 18 frontier models: ALL degrade with long context. Effective context can drop up to 99% below advertised. But prompt caching saves 41-80%. This is the guide I wish I'd read before burning $2,000 in tokens.
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?
85% per-step accuracy sounds good. But in a 10-step workflow, that's 20% success. Gartner predicts 40%+ of agentic AI projects will be scrapped by 2027. The fix isn't a better model — it's engineering I wish I'd learned sooner.
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.
Stanford proved that changing only the harness — without touching the model — creates up to 6x performance swings. Meta-Harness ranked #1 on TerminalBench-2, beating all human solutions. The problem was never the model.
In 2024, AI hallucinations cost $67.4 billion globally. A lawyer was sanctioned for citing cases invented by ChatGPT. A 2025 mathematical proof confirmed: hallucinations CANNOT be eliminated. Why?