Connor Shorten

Connor Shorten Verified account @CShorten30 · 2d

What do Rerankers and Chess have in common? ♟️

I am SUPER EXCITED to publish the 150th episode of the Weaviate Podcast featuring Ghita Houir Alami (@ghita__ha), Co-Founder of ZeroEntropy and now leading Model Research at Notion! 🔥

This is one of my favorite deep dive podcasts into Search! 🔎

We explored how pairwise preferences can be turned into useful pointwise scores with ZeroEntropy's zELO method! Like chess Elo, a sparse sample of pairwise battles between documents is enough to fit an absolute score through a Thurstone model, and that signal is then distilled into a pointwise reranker. The result... 🥁 ... the state-of-the-art zerank-2 cross encoder! 🔥

The podcast covers a lot of interesting topics in Search! I loved learning about Reranking with Jev / Decision Models. 🛠️

Ghita explained how many teams want cross encoders to prune and classify rather than just order results. Calibrated relevance scores can support searching with a Dynamic K, thresholding, and classification. 🧠

We also discussed the tradeoffs between pointwise, pairwise, and listwise ranking. ZeroEntropy's pairwise reranker was slightly more accurate, but distilling it into a pointwise model came close to matching it at much lower inference cost. ⚡️

We also covered infrastructure for rerankers, agentic search and reranking, instruction-following rerankers, metadata in training rerankers, and the relationship between RSI and SLMs! 📜

This was a super fun conversation, and I hope you find it useful! 💚

YouTube: youtube.com/watch?v=mQgYPt…
Spotify: spotifycreators-web.app.link/e/x1uM285CV7b

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Mert Cemri Verified account @mertcemri · Nov 4

What if you could synthesize an LLM inference engine customized for your model and Mac, just in time?

Meet JITEngine: custom engines built with SkyDiscover for Apple Silicon, up to 2.7× faster than the fastest alternatives we tested.

jitengine.ai 🧵 (1/n)

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Connor Shorten Verified account @CShorten30 · Nov 2

Scaling Discovery requires Forgetting! 🌊

I am SUPER EXCITED to publish the 149th episode of the Weaviate Podcast featuring Shangyin Tan (@ShangyinT), a Ph.D. student @UCBerkeley! 🔥

Long-running optimizers stall when an agent remembers too much. Forgetting can get them exploring again! 🏔️

The story starts with GEPA and its successor, `optimize_anything`, which extends these ideas from prompt optimization to code, GPU kernels, and agent harnesses! 🚀

Shangyin explains why prompts and harnesses are really a generalization test, whereas a single kernel is pure optimization. I loved this nugget for understanding how optimization algorithms are extending from models to code. 💡

Running GEPA, Autoresearch, and Meta-Harness as backends behind `optimize_anything` resulted in every one of them plateauing a couple of hours in.

As sessions grew, agents went from editing 100s of lines to 5 or 6 due to being stuck in a local optima. Handing only the best candidate to a fresh optimizer and forgetting the history got progress climbing again. 📈

We also discussed how to design Multi-Agent teams, the use of Git as a communication protocol for Multi-Agent AI, and how to handle expensive evals! 💸

This was a super conversation, I learned so much from Shangyin, and I really hope you find it interesting!

YouTube: youtu.be/Y21NgZvx3Rs
Spotify: spotifycreators-web.app.link/e/m3JjBPT0J7b

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Rikiya Takehi @rikiyatakehi · Nov 2

We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.

We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵
Blog: rikiyat.github.io/blog/weight-sp…

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