[D] Self-Promotion Thread

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[u/www3cam]

I’d love to talk to others at the cutting edge of ML applying it to other fields. I’m mainly interested in economics, but have interest across other social sciences and even math but my pure math is not good enough to publish there atm. Would be interested in like computational physics, biology and chemistry as well.

Edit: Or even humanities like English, history and philosophy although computational work in those areas seem more nascent.

[u/0RootShell]

I’ve started an AI Infrastructure Knowledge Base https://ai-infrastructure.net/ covering GPU clusters, distributed training, inference, RL/post-training, performance engineering and agent infrastructure.

Still very much a work in progress. Feedback and contributions welcome.

[u/reivblaze → u/0RootShell]

Interesting! Are you experienced in the field or looking to learn along the way? Would you add cuda programming?

[u/Ali-WAIL]

I've been working on WAIL, a runtime control layer for AI applications.

The problem I was trying to deal with is when an AI request technically succeeds, but the execution itself isn't healthy — latency suddenly gets much worse, token delivery becomes unstable, repeated failures start showing up, etc.

WAIL wraps the provider client and watches the runtime behavior. If things degrade enough, it can make a decision to retry or reroute the next request, and it records signed evidence of what it observed and why the decision was made.

It currently supports OpenAI, Anthropic, Gemini, OpenRouter, Ollama and OpenAI-compatible runtimes. It runs in your own environment, so prompts, responses and API keys aren't sent to WAIL.

There's a free Developer plan. Pro is $249/month, and Enterprise is custom.

GitHub: https://github.com/wailinfra/wail-runtime

PyPI: pip install wail-runtime

I'm the founder, so obviously this is self-promotion — but that's what this thread is for :)

[u/Lazy_Signature_9886]

Built Dejavu, a local memory layer for coding agents (Claude Code, Cursor, etc). Free, open source, MIT licensed. No pricing, no signup, nothing to buy.

The problem: agents lose everything between sessions. You end up re-explaining the same architecture decisions and known pitfalls every time you start fresh. Dejavu lets an agent write short typed notes ("slips") as it works, tagged as decisions, pitfalls, preferences, or work-in-progress, scoped automatically to the current git repo so unrelated projects never leak into each other.

The part I expect this sub to have opinions on: no embeddings, no vector DB. Recall runs on SQLite's FTS5 with BM25 ranking, fully deterministic, and auditable as plain SQL rather than a cosine similarity you have to trust. Every recall is capped to a fixed token budget so it can't flood the agent's context window. Trust is tracked separately from relevance too, based on how often a note gets confirmed useful vs. marked wrong, so a note matching your query text isn't automatically treated as correct.

The tradeoff is real. BM25 misses paraphrase gaps an embedding model would catch. For a repo-scoped corpus of a few hundred notes, written by agents in fairly consistent technical shorthand, I think it's the right bet for now, but I'm open to being told otherwise.

Repo: github.com/sanjayrohith/Dejavu