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submitted by /u/esporx [link] [comments]

I've been building long-running agentic workflows and kept hitting the same problem: the AI forgets instructions from earlier in the conversation, rushes to produce output, and skips boring middle steps. The research explains why: "Lost in the Middle" (Stanford 2023) showed a 30%+ performance drop when critical information is in the middle of the context window. Accuracy is high at the start and end, drops in the middle. Exactly like working memory overflow. "LLMs Get Lost in Multi-Turn Conversation" (Laban et al. 2025) showed that instructions from early turns get diluted by later content. The more turns, the worse the recall. 65% of enterprise AI failures in 2025 were attributed to context drift during multi-step reasoning. The parallel to ADHD executive dysfunction isn't metaphorical. ...

submitted by /u/sksarkpoes3 [link] [comments]

I work as a developer, and before this I was copium about AI, it was a form of self defense. But in Dec 2025 I bought subscriptions to gpt codex and claude. And honestly the impact was so strong that I still haven't recovered, I've barely written any code by hand since I bought the subscription And it's not that AI is better code than me. The point is that AI is replacing intellectual activity itself. This is absolutely not the same as automated machines in factories replacing human labor Neural networks aren't just about automating code, they're about automating intelligence as a whole. This is what AI really is. Any new tasks that arise can, in principle, be automated by a neural network. It's not a machine, not a calculator, not an assembly line, it's automation of intelligence in the b...

I built a pipeline where 5 AI models (Claude, GPT-4o, Gemini, Grok, DeepSeek) independently assess the probability of 30+ crisis scenarios twice daily. None of them see the others' outputs. An orchestrator synthesizes their reasoning into final projections. Some observations after 15 days of continuous operation: The models frequently disagree, sometimes by 25+ points. Grok tends to run hot on scenarios with OSINT signals. The orchestrator has to resolve these tensions every cycle. The models anchored to their own previous outputs when shown current probabilities, so I made them blind. Named rules in prompts became shortcuts the models cited instead of actually reasoning. Google Search grounding prevented source hallucination but not content hallucination, the model fabricated a $138...

Open source: https://github.com/htdt/godogen submitted by /u/crush-name [link] [comments]

submitted by /u/Tiny-Independent273 [link] [comments]

submitted by /u/PixeledPathogen [link] [comments]

submitted by /u/stvlsn [link] [comments]