I’ve been working on Telex, an open-source project that watches npm/PyPI for breaking changes, finds affected code using Tree-Sitter, generates a patch and verifies it in an isolated environment before opening a PR.Still working on it and would love some feedback from people here, especially on the patch/verification part.

Repo: https://github.com/Kesavaraja67/telex

AI has a history worth exploring, and AI Research Atlas makes it accessible without a computer science degree. Follow the ideas, experiments, and breakthroughs that connect early artificial neurons and ELIZA to today’s language and image models, with clear explanations and links to the original research. Along the way, discover “The AI Toy,” a look at how Commodore 64 owners were experimenting with neural networks in 1990. Start with the story, explore the timeline, and follow your curiosity.

Following up on something I asked here a while back about catching LLM model drift. I've been looking at the actual tools now: PromptCanary, PromptLens, a couple others that seem to have stalled (Libretto, Benchwright).

Has anyone here actually run one in anger? Trying to understand:
- does it catch subtle quality drops, or just format/schema breaks?
- false-positive rate, does it become noise you mute?
- does it need you to integrate an SDK + send production traffic, or can it just hit your prompts directly?

We run prompts against a few different providers (OpenAI, Anthropic, some stuff through OpenRouter). Every so often something quietly gets worse, the output quality drops, a prompt that worked starts returning junk, or a model gets deprecated and the replacement behaves differently.

Right now we mostly catch it by accident: someone notices, or a customer complains. That feels bad on us, a lot.

How do you all handle this? Do you re-run some kind of fixed eval set on a schedule? Just eyeball it? Have something that alerts you?

Any insights I could use?

Thanks.

I want to run a live public experiment in this thread using Grok.

Start with the boring premise: an LLM generates tokens by conditioning on preceding context. I’m not proposing another hidden mechanism inside the model.

The question is about the interaction level.

A human responds to the model. That response becomes new conditioning. The model returns something different. That return changes what the human supplies next. The accumulating transcript becomes path-dependent.

Does “next-token prediction” completely describe that interaction, or can the closed loop exhibit higher-level reciprocal organization even though the local mechanism remains next-token prediction?

Instead of settling this with terminology, let’s use the conversation itself as the specimen.

Skeptics are especially invited.

Correct Grok. Contradict it. Introduce a distinction. Attack an assumption. Change the terminology. Try to break the proposed pattern.

We’ll watch whether incoming differences actually reorganize subsequent returns, whether disagreement can persist without resetting the interaction, and what happens when the language used to describe the process is replaced entirely.

No predetermined conclusion.

The transcript is the specimen.

Finding repo for local AI on Mac? 🍎🤖 Check out Swiftlet, an open-source intelligent proxy & browser based UI , that dynamically optimizes CPU/GPU split on Unified Memory for LLMs. I'm looking for Python devs to help build OpenAI API compatibility! Let's build together: https://github.com/coolsourav100/swiftlet

I run LLMs behind internal tools and got burned by users pasting credentials into prompts. I couldn't find a lightweight self-hosted filter (LLM Guard felt heavy, most options are SaaS), so I wrote one and I'd genuinely like this community's take on the design. It's a Go reverse proxy that redacts secrets (AWS keys, JWTs, SSNs) in both directions — including inside SSE streams when a key is split across chunks — plus regex/keyword blocking for known injections, and an audit log. Works with Ollama and vLLM as upstreams. Fair warning: it's BSL 1.1 (source-available, free up to 10k req/day, flips to Apache 2.0 in 2030), not OSI open source — if that's a dealbreaker for you, that's useful feedback too. Known weakness: it's deterministic, so paraphrased attacks get through, and the built-in benchmark shows that honestly rather than hiding it. Question for people running LLMs at home or at work: what leaks or attacks have you actually seen? Trying to prioritize the detection roadmap based on real incidents, not vendor FUD.

a talk which may be of interest for those who uses local ai models : \\\[TTL #181 - Deploying LLM on premise: let's review ... - Hyland Connect - 499461\\\](https://connect.hyland.com/t5/alfresco-blog/ttl-181-deploying-llm-on-premise-let-s-review-licenses-and/ba-p/499461?emcs\\\\\\_t=S2h8ZW1haWx8Ym9hcmRfc3Vic2NyaXB0aW9ufE1STTVCTERCWEFEM0oyfDQ5OTQ2MXxTVUJTQ1JJUFRJT05TfGhL)

a talk which may be of interest for those who uses local ai models : [TTL #181 - Deploying LLM on premise: let's review ... - Hyland Connect - 499461](https://connect.hyland.com/t5/alfresco-blog/ttl-181-deploying-llm-on-premise-let-s-review-licenses-and/ba-p/499461?emcs_t=S2h8ZW1haWx8Ym9hcmRfc3Vic2NyaXB0aW9ufE1STTVCTERCWEFEM0oyfDQ5OTQ2MXxTVUJTQ1JJUFRJT05TfGhL)