the experiment is clean they took real multi-turn conversations from WildChat and ShareLM. not synthetic benchmarks. actual human-ai chats then they ran every conversation two ways across four models (Qwen3-4B, DeepSeek-R1-8B, GPT-OSS-20B, and GPT-5.2): > full context: normal.
GENERATIVE AI
-
AI Systems Store Full Conversation History
By
–
here's the assumption nobody questioned every chatbot, every agent, every multi-turn ai system stores the full conversation. your messages AND the model's own replies. stacked up turn after turn, fed back in as context every time you ask something new seems obvious. the model
-
MIT finds LLM context pollution degrades performance
By
–
MIT researchers discovered a phenomenon called "context pollution" where llms get WORSE by reading their own prior responses errors, hallucinations, and stylistic artifacts from earlier turns propagate forward because the model treats its own output as ground truth and removing
-
Work Dystopia: Fragile Scenarios, Necessary Economic Vigilance
By
–
Dystopian scenarios about the disappearance of work are making a comeback after a year-long lull. Largely fueled by the Citrini Research report predicting a worker catastrophe in the short term, these scenarios rest on an assumption of radical and excessive automation; the idea of recursive AI is also present.Both on its technological and economic fundamentals, the report and these scenarios are generally very fragile.In the longer term, however, we must remain vigilant. One of the central points of the digital economy is its capacity to concentrate capital and circumvent regulation. The capital-labor distortion introduced by globalization could therefore increase radically if we are not careful. This is the subject of my latest article in Les Échos. [Translated from EN to English]
→ View original post on X — @flashtweet, 2026-03-03 11:07 UTC
-
AI-Generated Fake War Videos: Need for Strategy
By
–
Too many AI generated fake war videos. Any strategy to stop this @nikitabier ?
→ View original post on X — @skathirmani, 2026-03-03 10:24 UTC
-
AI Coding: Voice Reduces Input Friction
By
–
The bottleneck in AI-assisted coding was never the model’s intelligence. It was input friction.
— God of Prompt (@godofprompt) 3 mars 2026
Humans speak at 150 words per minute. They type at 40.
Claude Code voice mode closes that 3.7x gap. You describe the bug, the architecture decision, the refactor. Claude writes and… https://t.co/BPxjWPj3pcThe bottleneck in AI-assisted coding was never the model’s intelligence. It was input friction. Humans speak at 150 words per minute. They type at 40. Claude Code voice mode closes that 3.7x gap. You describe the bug, the architecture decision, the refactor. Claude writes and
-
Using Claude prompts to accelerate book writing
By
–
Le livre que tu repousses depuis 3 ans peut être terminé en 72 heures. Ce qui t'en empêchait n'était pas le manque de temps. C'était de ne pas connaître ces 9 prompts Claude: [ Ajoutez en signet pour ne pas perdre ! ]
-
Using Claude Prompts to Accelerate Book Writing
By
–
Le livre que tu repousses depuis 3 ans peut être terminé en 72 heures. Ce qui t'en empêchait n'était pas le manque de temps. C'était de ne pas connaître ces 9 prompts Claude: [ Ajoutez en signet pour ne pas perdre ! ]
-
Efficient and Cost-Effective AI Model Execution Compared
By
–
it’s quite efficient to run — definitely cheaper than nano banana!
-
Qwen’s New Models Defy Expectations
By
–
Qwen just dropped 4 new models and the math doesn’t make sense.
— God of Prompt (@godofprompt) 3 mars 2026
> 4B nearly matches their previous 80B A3B
> 9B rivals GPT OSS 120B at 13x smaller
> 0.8B and 2B run on your phone
All free, offline, and open source
Let that sink in. https://t.co/1rqKBiiCgR pic.twitter.com/0pCYM8tRTjQwen just dropped 4 new models and the math doesn’t make sense. > 4B nearly matches their previous 80B A3B
> 9B rivals GPT OSS 120B at 13x smaller
> 0.8B and 2B run on your phone All free, offline, and open source Let that sink in.