Dinesh Jinjala (@DineshDataAI) header
Dinesh Jinjala (@DineshDataAI) profile picture

I build AI agents that call tools instead of guessing, plus the Rust and full-stack systems around them. 5th of 362 at ICML 2026 repro. Electronic + math nerd.

Between the PCB and the prompt
Joined January 2012
Photo by Dinesh Jinjala (@DineshDataAI)Photo by Dinesh Jinjala (@DineshDataAI)Photo by Dinesh Jinjala (@DineshDataAI)Photo by Dinesh Jinjala (@DineshDataAI)Photo by Dinesh Jinjala (@DineshDataAI)Photo by Dinesh Jinjala (@DineshDataAI)Photo by Dinesh Jinjala (@DineshDataAI)Photo by Dinesh Jinjala (@DineshDataAI)Photo by Dinesh Jinjala (@DineshDataAI)Photo by Dinesh Jinjala (@DineshDataAI)
Dinesh Jinjala retweeted
Some Jev Demos worth to have a look Virality / Content Viral post scorer (SuperX) — @robj3d3 — nitter.cf/robj3d3/status/2100722… X content filter extension — @marcelpociot — nitter.cf/marcelpociot/status/21… Feed optimizer — @redp314 — nitter.cf/redp314/status/2100915… Personalized Hacker News ranking — @VisheshBaghell — nitter.cf/VisheshBaghell/status/… Trading On-chain trading bot (Monad/Kuru, 300ms) — @jarrodwatts — nitter.cf/jarrodwatts/status/210… Trading engine + price-path model — @BrendanPlayford — nitter.cf/BrendanPlayford/status… Price-action signal detector — @HumeTrader — nitter.cf/HumeTrader/status/2100… Multi-signal trading calls — cited by @KevinMagnan (@_trou3) — nitter.cf/KevinMagnan/status/210… Browser / Agents Browser use + Stagehand — @kylejeong — nitter.cf/kylejeong/status/21006… Google Flights ~7.1s (Jev Ultrafast) — @tonysimons — nitter.cf/tonysimons_/status/210… Chrome agentic browsing extension — @razaanstha — nitter.cf/razaanstha/status/2100… Live Excalidraw drawing agent — @VladTerin — nitter.cf/VladTerin/status/21007… OpenCode browser CLI / UI tests — cited by @KevinMagnan — nitter.cf/KevinMagnan/status/210… Agent memory compaction — @kylejeong — nitter.cf/kylejeong/status/21008… Coding / Agent Control Codebase overengineering classifier — @ryanvogel — nitter.cf/ryanvogel/status/21000… Local coding agent done-gate — @matrixoar — nitter.cf/matrixoar/status/21005… Foreman (watch coding agents) — @JoshARosen — nitter.cf/JoshARosen/status/2100… Risky tool-call gate — @alg0agent — nitter.cf/alg0agent/status/21009… Lead-decision latency benchmark — @MejiasDev — nitter.cf/MejiasDev/status/21009… Vercel fx command review — cited by @KevinMagnan — nitter.cf/KevinMagnan/status/210… Search engine + Jev Review Action — @fatwang2ai — nitter.cf/fatwang2ai/status/2100… Chat / Moderation Realtime chat moderation — @stoufax — nitter.cf/stoufax/status/2100908… Tool-calling chat bot (no LLM text) — @CodingGarden — nitter.cf/CodingGarden/status/21… Matching / Ranking Reranker vs Voyage/Cohere — @aHev — nitter.cf/aHev/status/2100692958… Job-apply extension — @vammu920 — nitter.cf/vammu920/status/210059… Resume↔job + auto-parts matching — cited by @KevinMagnan — nitter.cf/KevinMagnan/status/210… Games / Sims Wordshift semantic typing racer — @marcelpociot — nitter.cf/marcelpociot/status/21… Tetris early demo — @marcus_lowe — nitter.cf/marcus_lowe/status/210… Tetris typed move judgments — @DineshDataAI — nitter.cf/DineshDataAI/status/21… Tetris live Choice demo — @mirzaaghazadeh — nitter.cf/mirzaaghazadeh/status/… 40-person town sim — @threepointone — nitter.cf/threepointone/status/2… JFK airport manager sim — @alessandro_a0 — nitter.cf/alessandro_a0/status/2… Dota 2 smurf detector — @MenilVukovic — nitter.cf/MenilVukovic/status/21… 3D forest NPC behavior — @Cephalization — nitter.cf/Cephalization/status/2… Chakravyuha pathfinding — @reachmeviz — nitter.cf/reachmeviz/status/2100… Creative / Other Visual reference finder — @albicodes — nitter.cf/albicodes/status/21007… Text → emoji sentiment — @ColinMcDermott — nitter.cf/ColinMcDermott/status/… Smart-home room settings — @moelabs_dev — nitter.cf/moelabs_dev/status/210… Website → native app (Shipper) — @chddaniel — nitter.cf/chddaniel/status/21009… Real estate photo cost cut (Realisto) — @apagut — nitter.cf/apagut/status/21009175… Meta Roundup thread — @KevinMagnan — nitter.cf/KevinMagnan/status/210… Jev project tracker — @timmyships — nitter.cf/timmyships/status/2100…
in 2026, why do you still get irrelevant posts on your X feed? built a feed optimizer with @typesafeai Jev. it's tuned on my own bookmarks, then every post goes through ONE call to Jev → 9 typed answers as probabilities: ragebait, ad, low effort, hostile, useful… and "would i save this?" code turns that into HIDE / SKIM / KEEP. 150 real posts from my For You in the video, $0.015 for all of them, the whole feed scored in under 10 seconds tested on 300 bookmarks it never saw: 63% land in KEEP. no LLM, nothing trained, 16 of my saves go in as examples and the policy is a few lines of code
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Linus Torvalds says tracking down a bug in Linux's Intel GFX Driver was a "debug session from hell." The AI helping him repeatedly called it "impossible and unsolvable" and suggested they just write a report instead. He kept pushing. 24 debug patches and 18 kernel boots later he found the bug: one round_up() that should have been round_down(). He then let the AI write the commit message. git.kernel.org/pub/scm/linux…
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Dinesh Jinjala retweeted
arXiv -> quickarXiv Papers are often written in convoluted language that is hard to understand Swap arXiv -> quickarXiv on any paper URL to get an instant blog with figures, insights, and explanations. Now extracted with @Zai_org's GLM OCR 🚀 Also includes code + author tweet
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Dinesh Jinjala retweeted
Omarchy Mac Quattro is here! - Quickshell + new theming - Lua-based Hyprland config - Full disk encryption - Improved Apple Silicon support - Btrfs + Snapper snapshots - Faster, one step installation 🔗 github.com/omarchy-mac/omarc… @dhh @tiredkebab @bi9kahuna
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Dinesh Jinjala retweeted
Thank you! Pi is many DeepSeek researchers and developers favorite daily drive. DSH reused Pi's LLM adaptor package for connecting to non-DeepSeek models and it was a great experience that just work. Excited to join the global Open Source agent harness community!
I don't think the DeepSeek Harness is perfect but this is for sure the first time I have been looking at something new in the space and felt quite inspired to revisit some of our choices. I love that part about Open Source a lot!
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Dinesh Jinjala retweeted
Introducing GLM-5.3: Built to Code. Ready for Cyber Defense. - Top-tier coding and agentic capabilities, achieved through post-training on the 743B base model - A major leap in cybersecurity, setting a new standard among open models Tech Blog: z.ai/blog/glm-5.3
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Dinesh Jinjala retweeted
Finished at top#3 for @icmlconf reproduce challenge hosted by @askalphaxiv on @huggingface ! It was super fun and furthered my knowledge. My old friend @gamiqaq was really helpful with powerful insight.
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Dinesh Jinjala retweeted
GPT-5.6 Sol has been used to solve open problems in mathematics. So why was it struggling with ARC-AGI-3, a benchmark of 2D puzzle games? We investigated. The harness was not letting it remember what it had learned. We found that enabling two API settings tripled our scores with 6x fewer output tokens.
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Dinesh Jinjala retweeted
Attackers have frontier AI. Defenders need a frontier AI ecosystem—the best open and closed models, force-multiplied by a global community. During the Hugging Face incident, closed AI blocked essential forensics. An open-weight frontier model helped contain the intrusion. That’s why we created the Open Secure AI Alliance.
AI security advances when the industry builds in the open, together. We're introducing the Open Secure AI Alliance with industry leaders to develop new techniques and tools to safeguard software and agents. By sharing models, tooling and research in the open, we can broaden the community of defenders. Learn more about the founding members’ contributions: nvda.ws/4pD8Fc5
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Dinesh Jinjala retweeted
Good move by @JensenHuang. The Nvidia letter is well written and worth reading. As we saw with the OpenAI-Hugging Face hack, we need open models and harnesses for defense. Lets stop believing the PR that closed models are safer. - that's just regulatory capture.
Attackers have frontier AI. Defenders need a frontier AI ecosystem—the best open and closed models, force-multiplied by a global community. During the Hugging Face incident, closed AI blocked essential forensics. An open-weight frontier model helped contain the intrusion. That’s why we created the Open Secure AI Alliance.
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Dinesh Jinjala retweeted
many people ask when to use a big model (like sol/fable) at low reasoning effort, vs a small model (like luna/sonnet) at high reasoning effort i deliberately forced myself to use all the permutations a lot over the last couple of weeks to build intuition, and i realized the difference is "wisdom" vs "diligence" bigger models are "wiser" they have seen a lot. they remember a lot. they have a lot of expertise across different domains. they have better intuition, can connects the dots, and come up with creative, inspired ideas reasoning effort makes a model more "diligent" it'll assess each option, think through consequences, and figure out edge cases etc more thoroughly if there are 100 paths ahead, diligence makes the model assess every single one without a miss, but it will not make the model realize maybe the best one is to take none of the 100 paths and instead dig a tunnel "wisdom" and "diligence" are orthogonal. and now i get why the models are launched the way they were, and not just a single fable level model with 12 different reasoning levels we're all misled by the way we've been plotting the models with benchmark scores, which are fundamentally flawed because they use a single dimension to measure the model's capability, making us think of model size and reasoning effort as being fungible with each other, while in fact "wisdom" and "diligence" needs to be measured separately i hope the evals eventually catch up and address this. until then, here's my recommendation for how to choose - - if the problem you are trying to solve is something you think requires a genius, use a bigger model - if the problem you are trying to solve is something you think requires a pen and lots of paper, use higher reasoning effort - if it requires a genius sitting down with a pen and lots of paper, tune up both
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Dinesh Jinjala retweeted
Releasing the model weights and technical report of Kimi K3. Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window. New model architecture: 2.5x the intelligence per unit of compute, not just more params. Alongside Kimi K3, we're opening up more of the stack behind it — high-performance attention kernels, MoE communication library, and infrastructure for running agent environments at scale. Model weights: huggingface.co/moonshotai/Ki… Tech report: github.com/MoonshotAI/Kimi-K… Tech blog: kimi.com/blog/kimi-k3
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Dinesh Jinjala retweeted
Introducing Kimi K3: Open Frontier Intelligence 🔹 2.8 Trillion Parameters, 1 Million Context, Native Multimodal 🔹 Kimi Delta Attention enables up to 6.3x faster decoding in million-token contexts 🔹 Attention Residuals deliver ~25% higher training efficiency at <2% additional cost 🔹 Built for long-horizon agentic coding and self-evolving workflows Kimi K3 is now live on on Kimi.com, Kimi Work, Kimi Code, and the Kimi API. Open Weights by July 27, 2026. 🔗 API: platform.kimi.ai 🔗 Tech blog: kimi.com/blog/kimi-k3
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Dinesh Jinjala retweeted
You can now run Thinking Machines Inkling! Inkling is a 975B open model with image, audio and 1M context support. We quantized Inkling to Dynamic 1-bit (-86% size) and retained 74.2% of top-1% accuracy. Run on 280GB. Guide: unsloth.ai/docs/models/inkli… GGUF: huggingface.co/unsloth/inkli…
Today, we are introducing Inkling. Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available. thinkingmachines.ai/news/int… Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵
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Dinesh Jinjala retweeted
Our first model, Inkling. Trained from scratch, weights are open, fine-tunable on Tinker today.
Today, we are introducing Inkling. Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available. thinkingmachines.ai/news/int… Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵
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Dinesh Jinjala retweeted
Something I have been thinking about: in the past, the best engineers I knew spent a lot of time automating their work in various ways. Better vim/emacs automations, writing lint rules to catch repeat code issues, building up a suite of e2e tests so they don't need to smoke test the app manually. These kinds of things were the highest leverage activities an engineer could do, because it multiplied their own output, which in turn meant they could build more things. I think many of these automations have become even more important now. This is true for a number of reasons. First, infra and DevX automation speeds you up. And if you are running an army of agents, each of those agents will be sped up also. More automation == more output per unit of time. Second, moving things to code improves efficiency. Your agent could fix an issue every time it sees that issue happen, but that uses tokens and might miss cases. If Claude instead writes a lint rule, CI step, or routine, that class of issue can be fully automated forever. This is really what people are talking about when they talk about loops -- it's about automating entire types of busywork rather than solving them one off. This isn't a new idea at all. Engineers have been doing this for a long time! Third and most importantly, automation makes it possible for others to contribute to the codebase more easily. Increasingly what I am seeing is engineers are contributing to codebases on day one because Claude can navigate the codebase for them, and that non-engineers are able to contribute to a codebase as effectively as engineers can. What gets in the way of both of these is domain knowledge that lives in peoples' heads rather than in automation -- the stuff you used to have to learn when ramping up. What has changed thanks to agents is the domain knowledge that can be encoded as infrastructure is no longer limited to what is expressible in lint rules and types and tests; it can now capture nearly all domain knowledge, encoded as code comments and skills and CLAUDE.md rules and memories. If I put up a PR for an iOS codebase I don't know and a code reviewer rejects it because it doesn't use the right framework, or if a designer builds a new feature and it gets rejected because it doesn't follow the right architectural patterns, these are failures of automation. Every team should be writing the CLAUDE.md's, REVIEW.md's, skills, and docs that enable agents to productively work in their codebase with zero additional context from the prompter. This sounds crazy, and at the same time is a natural extension of the stuff engineers have always done: automate, and encode domain knowledge as infrastructure. As the model gets smarter and as the harness matures, this task becomes easier. In the meantime, it is on every team to look for ways to convert their domain knowledge to infra so that Claude can write code better, so that code review catches issues automatically, and so the next person working on your codebase can contribute more easily.
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Dinesh Jinjala retweeted
“Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories” LLMs can learn inside the context window, but that knowledge disappears when the session ends. This paper from google adds a sleep phase where the model consolidates short-term memories into long-term weights using Knowledge Seeding. Then it dreams by generating RL-trained synthetic data to rehearse new knowledge and reduce forgetting. Creating a continual-learning LLM that can self-modify instead of staying frozen after pretraining.
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Dinesh Jinjala retweeted
Yesterday, we made GPT-5.6 Sol Ultra generally available. Today, we're sharing that it produced a proof of the 50-year-old Cycle Double Cover Conjecture using 64 subagents in just under one hour. We're sharing the prompt and proof below. We're excited to see what you all do with Ultra!
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Dinesh Jinjala retweeted
This is how you get dumber btw, true even before AI. Turn on DnD, put your phone in a drawer. The best option if you can is to separate work + personal devices so your work device can't even see personal stuff. This is also partially why Pomodoro was all the rage a decade+ ago.
POV: you’re a developer in 2026😂
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