just a boy enjoying his agentic swarm. Stay databased. I like ACID.
Los Angeles, CA
Joined June 2020
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Saylor retweeted
i've been wanting to build something like this for a while.
imagine having a shared memory layer for all your coding agents, where every debugging session, workaround and correction becomes reusable knowledge
so you don't have to start from scratch every time you switch tools.
really interesting to see teams building this.
Our open-source devtool is trending #1 on Product Hunt!
Beacon is self-improving memory for coding agents.
🫶 I’d love your support: producthunt.com/products/asy…
→ Turn past debugging sessions into reusable skills
→ Share learnings across Claude Code, Cursor, Codex, and 20+ harnesses
→ Search and replay past sessions
→ Stop wasting tokens rediscovering solutions
→ Build a feedback loop that makes your agents better over time
→ Free, open source, and local-first
We built Beacon because we were tired of teaching our agents the same things over and over, only for them to start from zero every new session.
Every agent session should make the next one smarter.
⭐️ Star us on GitHub: github.com/Asymptote-Labs/ag…
Saylor retweeted
Misreading the Bitter Lesson is how agents end up burning fortunes rebuilding context.
Expensive amnesia, paid to anthropic in tokens.
The fix: semantic state at ingestion, ontology at retrieval, tiny models for traversal, frontier models for judgment.
Saylor retweeted
Introducing SpectralQuant.. here to save your KV cache :)
@sentra_app just killed @GoogleResearch's TurboQuant.
SpectralQuant — 5.95× KV cache compression on Mistral 7B at +7.5% perplexity overhead.
TurboQuant at the same compression: +22%.
3× less degradation. 15-second calibration. One per-model, then drop-in for any HuggingFace LLM, ViT, ESM, AlphaFold Evoformer, or VideoMAE.
Check out the findings and how the mechanism works below. ↓
Saylor retweeted
We've open-sourced the whole thing, code, data, and results. Take a look:
github.com/Dynamis-Labs/spec…
Saylor retweeted
@sentra_app just killed @GoogleResearch's TurboQuant.
Introducing SpectralQuant — 5.95× KV cache compression on Mistral 7B at +7.5% perplexity overhead.
TurboQuant at the same compression: +22%.
3× less degradation. 15-second calibration. One per-model, then drop-in for any HuggingFace LLM, ViT, ESM, AlphaFold Evoformer, or VideoMAE.
📰 Paper & results: github.com/Dynamis-Labs/spec…
Check out the findings and how the mechanism works below. ↓
Saylor retweeted
Latency dropped from 2+ seconds (waiting on the embedding API) to <10ms. Zero token cost for memory consolidation. Semantic accuracy actually improved. By averaging the vectors directly, we stopped introducing the linguistic 'noise' of forcing an LLM to summarize the concepts first.
Saylor retweeted
Just dropped with my researcher @anirudhbv_ce
“The Geometry of Failure”, an autopsy of Google’s flagship multimodal embedding model gemini-embedding-2 (May 2026).
We mapped its actual geometry across text/image/audio/video to stress-tested every classic RAG failure mode.
Saylor retweeted
github.com/kruschdev/krusch-…
Just updated the overnight memory consolidation.
Saylor retweeted
We finally know why LLMs hallucinate. It's not the model. It's the geometry.
@OpenAI text-embedding-3-large: 91/3072 dimensions do real work.
@GeminiApp gemini-embedding-001: 80/3072 dimensions do real work.
~97% of your vector database is mathematically empty. Your RAG system is retrieving from noise.
@ashwingop and I present "The Geometry of Consolidation" - a proof that RAG compression has a hard floor no algorithm can beat, set by a single spectral number your embedding model cannot escape.
Every hallucination your RAG pipeline produces? This is why.
Paper + results: github.com/niashwin/geometry…
Saylor retweeted
Most AI agents fail not because of bad models, but because of bad data routing. Calling five APIs sequentially adds 2-4 seconds of latency per decision.
Agents need unified context, not stitched responses.
Saylor retweeted
I actually made something that is local and opensource just for this reason.
github.com/kruschdev/krusch_…
