Prashanth Rao

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Prashanth Rao

Prashanth Rao

@tech_optimist

AI engineer @lancedb | prev @kuzudb. Blogging @ https://t.co/gLektr01zQ

Toronto 🇨🇦 Katılım Ekim 2013
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Prashanth Rao
Prashanth Rao@tech_optimist·
Great summary of Codex's product history, and how important evolving/pivoting rapidly in a chaotic world is proving to be (at least whenever agents are involved)
Dan Shipper 📧@danshipper

not surprising that @OpenAI is firing on all cylinders right now, and it’s an unbelievably interesting story - they launched GPT-5 summer of 2025 and positioned it as a pair programmer. we wrote at the time @every that they completely missed the new agentic coding that was starting to happen inside of Claude Code. they bet on agentic coding in the browser / vms and vibe coding in ChatGPT but it was too early - a small team broke off and began working on a separate Codex model line and product. didn’t have to serve the gigantic customer base of ChatGPT, and by November / December of 2025 with 5.3 it was clear they were starting to make something good and the progress was fast - Codex Desktop app launched in Feb and was just clearly superior. there’s a weird late comer advantage sometimes in AI because you get to skip to what works instead of your product having the scars of new capability improvements being bolted on every 3 months - Codex started getting popular it was clear it was a thing, and needed to get merged back in. Which they did, imo quite well—a super complicated thing that would’ve been very easy to screw up. most companies try to disrupt themselves and fail. OpenAI somehow figured out how to disrupt their main product, and then merge it back in seamlessly. incredible aura

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Tibo
Tibo@thsottiaux·
Now that we merged ChatGPT and Codex, what should we merge next? What's the double or nothing move.
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Prashanth Rao
Prashanth Rao@tech_optimist·
@dhruv___anand Yes, but with columnar storage, schema, SQL, versioning and all the other "lakehousey" goodness 😋
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Prashanth Rao
Prashanth Rao@tech_optimist·
It might seem strange to care about this, but there are genuine use cases in the wild where you need 𝘁𝗲𝗻𝘀 𝗼𝗿 𝗵𝘂𝗻𝗱𝗿𝗲𝗱𝘀 𝗼𝗳 𝘁𝗵𝗼𝘂𝘀𝗮𝗻𝗱𝘀 of columns in your table. 🤯 At that scale, even opening the table and reading its schema can be a bottleneck. 🧵👇🏽 1/3
LanceDB@lancedb

Lance now opens a 65K column table in 17 milliseconds. It used to take 17s. That's 1,032x faster 🚀 Wide tables are no longer a cold start problem. Amazing work by @xuanwo! 👏

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Prashanth Rao
Prashanth Rao@tech_optimist·
@dhruv___anand Can't share too much on that (yet)! But I'm hearing talk of 1M columns in a table 😳
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Dhruv Anand
Dhruv Anand@dhruv___anand·
@tech_optimist would love to know what sort of data is being stored in such tables
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Prashanth Rao
Prashanth Rao@tech_optimist·
Now, you can open these really wide tables MUCH faster, too. Latest benchmarks show that a 65K column table is opened in just 𝟭𝟳𝗺𝘀 (that's a >𝟭𝟬𝟬𝟬𝘅 speedup from before). Amazing work as always by @xuanwo! 🚀🚀 3/3 github.com/lance-format/l…
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Prashanth Rao
Prashanth Rao@tech_optimist·
Lance is uniquely positioned to handle these types of scenarios, because you can easily grow tables to those many columns in the first place (zero-cost data evolution where you can add as many columns as you like, only writing data for that column). 2/3 lance.org/guide/data_evo…
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Prashanth Rao
Prashanth Rao@tech_optimist·
@Keivansamani @DSPyOSS Yeah it surprised me how much better Gemini models are at OCR than much more expensive GPT/Claude models! I think fine-tuning is an underrated GOAT for OCR, tho. More ground to be covered there.
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Keivan
Keivan@Keivansamani·
@tech_optimist @DSPyOSS Fun! And yes I’ve found Gemini flash to be a killer at OCR! Looking forward to Google releasing more models…
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Prashanth Rao
Prashanth Rao@tech_optimist·
I went down the @DSPyOSS + GEPA rabbit hole again, and it's just fascinating how much the metric you define matters! 🚀 In this latest post, I explored what we can get out of prompt space alone for improving OCR outcomes with GEPA. 🧵👇🏽 1/10 lancedb.com/blog/make-hand…
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Prashanth Rao
Prashanth Rao@tech_optimist·
Been brainstorming with Sol (extra high) using the skill linked below, and it's just nailing everything I throw at it. github.com/prrao87/agent-… I think we're in a golden age of model improvement, there's some serious recursive shit going on. Let's make hay while the sun shines!
Isaac Miller 🧩@isaacbmiller1

First Sol impressions are REALLY good. Seems better than fable at taking a fuzzy idea and maintaining the thread throughout a conversation. Tis yet early for a real judgement

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Prashanth Rao
Prashanth Rao@tech_optimist·
@isaacbmiller1 Seriously! It was a lifesaver with the way it helped me explore a swathe of scoring metric strategies with GEPA lately, I don't know how I'd have gotten where I did without it!
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Isaac Miller 🧩
Isaac Miller 🧩@isaacbmiller1·
Sol might be the goat? After 2 days it's still nailing tasks. Will see if it continues.
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Prashanth Rao
Prashanth Rao@tech_optimist·
As a next step, it would make sense to combine an optimization run with fine-tuning the model to see even more gains. OCR (esp. on narrow domain data) is a challenging task that requires more than one way to skin the cat. @lancedb is the multimodal data management layer for the whole OCR improvement loop, all the way from curation, to feature engineering, to running the experiments on optimization (and fine-tuning, if required). 9/10 lancedb.com/blog/make-hand…
Prashanth Rao tweet media
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