spent the morning writing some go by hand. I haven't written code by hand for pretty much 2 years.

I am actually surprised I don't seem to have lost any of it. I just loathe spending time at the computer for this nonsense.

It really drove home how much the act of sitting at a computer and typing actually shuts down my thinking.

I'm sure some people feel a more intimate connection to their intellect this way, but I need the spatial component / the diagrams / the little graphical symbols than just raw alphabet soup.

#llms #llm #vibecoding

I found one GUI ssh key manager, #KeyMaster written in Vala which is new to me (part of the Gnome project) but KM is vibe coded using Claude, including a complete rewrite from Python.

I'm stearing clear of folk using #LLMs to code and think anyone using it for security is a danger to all of us. Just my opinion.

It's also sad to see so much suspect slop in the search results while looking for info on this. This stuff is known to have many major errors and it is ruining everything. #StopSlop!

Absolutely TRUE!!!

"If a credible Western open frontier player does not emerge, the consequences cascade quickly.

This is the inverse of the early Internet wave. In the 2000s and 2010s, Western companies — Google, Facebook, Amazon, Apple, Microsoft — dominated globally while China carved out its own walled garden. The AI version flips that dynamic on its head. Without a credible Western open frontier player, the only open models capable of running entire economies are made in China. If U.S. policy further restricts Chinese open-weight access on national-security grounds, the U.S. ends up with two or three closed Cathedrals serving the U.S. market — and the rest of the world picks the AI stack that is free, capable, self-hostable, and not embargoed. Europe, Africa, Southeast Asia, Latin America, India, the Middle East. Roughly six billion people. Chinese open models become the global default by 2030, and the United States ends up technologically isolated from the majority of the world’s AI users. We would have done it to ourselves.

Watch what happens to AI infrastructure over the next twenty-four months. And watch Washington just as carefully.

Open source is no longer just how good software gets built. It is how dominant incumbents get neutralized, how trillion-dollar industries shift their power structure, and how the next generation of strategic moats gets dug — by the companies smart enough to dig them in the open."

https://p3institute.substack.com/p/from-open-source-software-to-open?source=queue

#OpenSource #FLOSS #China #AI #USA #GenerativeAI #OpenWeights #LLMs

From Open Source Software to Open Source Strategy

How the Smartest Executives Are Using Open Source Techniques to Optimize Corporate Strategy

Bill's Substack
if you like dark minimal melodic electronica
then check out my tune Mitril
https://vasnic.wtnet9.site/?p=&s=Mitril
#music #videogames #tune #llms
Mitril - Posts - Vasnic

Vasnic a Social Community For Everyone!

I Tracked Down the Hidden Workers Secretly Powering ChatGPT

YouTube

Reflexion splits self-correction in two: an Evaluator that detects success/failure, and a Self-Reflection model that diagnoses what went wrong. The Evaluator's external signal — heuristic, exact-match, or test execution — gates whether diagnosis fires. When that signal misfires, as on MBPP Python's high false-negative rate, Self-Reflection rewrites correct code wrong, exactly the failure mode Cannot-Self-Correct documented.

https://benjaminhan.net/posts/20260516-reflexion/?utm_source=mastodon&utm_medium=social

#LLMs #AI #Reasoning #Agents #Metacognition

Reflexion: Language Agents with Verbal Reinforcement Learning – synesis

An LLM agent that converts environment feedback into natural-language reflections stored in episodic memory beats strong baselines on AlfWorld, HotPotQA, and HumanEval without updating any weights.

synesis

Cannot-Self-Correct tests the strong claim that LLMs can revise their own reasoning answers without any external signal about correctness. Across three benchmarks (GSM8K, CommonSenseQA, HotPotQA), the answer is no: the model's confidence carries over from the initial answer into the revision, and the self-correction loop tends to degrade rather than improve performance. The result refutes the class of approach Self-Refine belongs to.

https://benjaminhan.net/posts/20260516-cannot-self-correct/?utm_source=mastodon&utm_medium=social

#LLMs #AI #Reasoning #Metacognition

Large Language Models Cannot Self-Correct Reasoning Yet – synesis

A critical examination of intrinsic self-correction shows that without external feedback, LLMs degrade rather than improve their reasoning answers across GSM8K, CommonSenseQA, and HotpotQA.

synesis

In Self-Refine, a single frozen LLM acts as generator, critic, and rewriter in a prompt-only loop, and the paper reports about 20 points of average lift across seven tasks without any training, RL, or external signal. The gains vary widely by task: small on math reasoning, but large on dialogue and constrained generation, where what counts as "good" is hardest to define from a one-line critique.

https://benjaminhan.net/posts/20260516-self-refine/?utm_source=mastodon&utm_medium=social

#SelfRefine #LLMs #AI #Reasoning #Metacognition

Self-Refine: Iterative Refinement with Self-Feedback – synesis

A single LLM acts as generator, feedback provider, and refiner in a prompt-only loop, improving outputs on seven diverse tasks without any additional training.

synesis

This is a 3-paper arc on whether LLMs can reliably self-correct their own reasoning. Self-Refine proposes a naive intrinsic-feedback loop and reports impressive gains. Cannot-Self-Correct refutes empirically the class of approach Self-Refine belongs to. Reflexion threads the needle by gating self-correction on a reliable external signal.

#LLMs #AI #Reasoning #Metacognition

«KI-Papers bei arXiv — Sperre bei erstem Verstoß:
Die Wissenschaftsplattform verschärft erneut ihre Regeln - Wer KI-Müll als Wissenschaft ausgibt, wird gesperrt - und danach genauer geprüft.»

Wie nun arXiv vorgeht kommt nun m.M.n. nicht überraschend und ist zu befürworten dass es geprüft wird.

https://www.heise.de/news/KI-Papers-bei-arXiv-Sperre-bei-erstem-Verstoss-11296035.html

#arxiv #kimull #aislop #wissenschaft #ai #aipaper #ki #fehlinformationen #realitat #LLMs #unwissen #unwissenschaftliches #sperrung

KI-Papers bei arXiv: Sperre bei erstem Verstoß

Die Wissenschaftsplattform verschärft erneut ihre Regeln: Wer KI-Müll als Wissenschaft ausgibt, wird gesperrt – und danach genauer geprüft.

heise online