Christopher Manning (@chrmanning) header
Christopher Manning (@chrmanning) profile picture

Founder @stanfordnlp & cs224n—Senior Fellow @StanfordHAI—Prof. CS & Linguistics @Stanford—GP @aixventureshq—MTS @moonlake—Australian🇦🇺—Do #NLProc & #AI 👋

Palo Alto
Joined September 2014
Photo by Christopher Manning (@chrmanning)Photo by Christopher Manning (@chrmanning)Photo by Christopher Manning (@chrmanning)Photo by Christopher Manning (@chrmanning)Photo by Christopher Manning (@chrmanning)Photo by Christopher Manning (@chrmanning)Photo by Christopher Manning (@chrmanning)Photo by Christopher Manning (@chrmanning)Photo by Christopher Manning (@chrmanning)Photo by Christopher Manning (@chrmanning)
Christopher Manning retweeted
The future of software In the limit, of course, software won't mean anything anymore. Everything digital will be instantly created at will. But in the interim period we'll wander through an increasingly odd in-between age for software. This transition periodwill be defined by three new precepts. Everflux software - instead of stepping from release to release, months apart, software now evolves continuously. Software creation has broken the sound barrier of our own keystrokes, and every model release pushes it faster. There is no reason to wait weeks or months for the next release when a single company merges over 30,000 code changes a day. Cyber capabilities are also progressing at an increasing pace. Bug and vulnerability fixes need to ship quickly and continuously. And every piece of software sits in a lattice of dependencies, each one now shipping at a blistering pace. Software will have to move accordingly fast just to keep from breaking as everything around it shifts. Translucent sources – A new territory has opened between open source and closed source. Today Claude will happily pick apart any Electron app, and any binary shipped with debug symbols has become a reverse-engineering playground for LLMs. In other words, much so-called closed-source software is now open source in all but name, and keeping code closed makes less and less sense. Even the notion of IP and copyright is breaking in software: Apps don't even need to be accessed to be reproduced. I'm writing this text on a personal text editor I prompted Claude to vibe-code from a few videos of people demoing editor features they liked. A mere description of the interface is increasingly enough for an LLM to rebuild the internals, whether or not they resemble the original. Software IP seems to be dissolving like cotton candy in a raccoon's paws. Bespoke features - Now that all software is open source, nothing prevents us from modifying everything. And personalization is just one prompt and a couple million tokens away, so there is absolutely no reason anymore to wait for someone else to add this feature you want. Everyone becomes their own product manager. Want a search box, an API bridge, a custom button? It's a couple of minutes away, mostly executed in the background of your daily life. Soon we'll want to personalize everything. A major challenge appears when bespoke features crash into our first tenet: everflux software. How do you customize software without cutting yourself off from upstream? I could fork it for good to add my search box, but I'd lose all future benefit from the software creator, be it new security updates or other updates to the rest of the software. How do you personalize software that was never meant to be personalized, without shouldering all of its future maintenance? Merge conflicts won't be the exception anymore; they'll be a constant of everflux software. Agents will rebase our personal features onto each new release overnight, and software will look less like a product and more like a living species, evolving through constant mutation. Image: M.C. Escher, Metamorphosis II, November 1939 - March 1940
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Christopher Manning retweeted
The RL era of robotics is here, and simulation is how to scale it. An early look at our research on a achieving a better scaling paradigm for robot learning with simulation 👇
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Christopher Manning retweeted
1/ Two years ago I scrolled to the bottom of the ICLR rankings and found art. This year I downloaded 125,680 ICLR 2027 reviews the day after they went public. There was more art 🧵
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I’d seen 2 papers not by me, in the (supposed) Intl Journal of Computer Vision and AI Applications and Intl Journal of Clinical and Medical Sciences. There have long been scam journals; this is a worse scam. The main thing is to avoid indexing them! Please, Google Scholar! 🙏
A group of academic journals has been publishing “AI slop” papers under the names of professors who didn't write them, sparking concerns that academics’ identities are being misused. Stanford computer scientists Fei-Fei Li and Christopher Manning are named in the journals
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Christopher Manning retweeted
This is a cool app. The only thing I share with my long-time friends these days is recipes and food pics. Look forward to using the app!
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Plug for my wife's new project! @jinpa1345 and her former Google colleague Matthieu have been working hard and have released apinch: a recipe manager where you can also follow what your friends are cooking, like Strava for food.
My former Google colleague Matthieu and I have launched our new recipe and cooking app, apinch! It's a recipe manager where you can follow what your friends are cooking – a Strava for home cooks. Load old recipe cards; add what you see on Insta; record what works for you!
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Christopher Manning retweeted
A real case can still be the wrong answer. One public tracker lists 2,266 court decisions where courts found or implied reliance on AI-generated material. 960 are tagged for real authority misrepresented, an error that can pass any "does this case exist?" check. A thread 🧵
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Christopher Manning retweeted
What's next for LLM research? @chrmanning, Professor of Machine Learning at Stanford, is joining us at All-Gather to break down the history and future of the field. Chris has spent decades shaping NLP at Stanford, from GloVe to CS224N, the course that taught a generation of researchers how language models work. baseten.co/all-gather
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Christopher Manning retweeted
I agree with @mustafasuleyman here. I think it is a slippery slope, with lots of very undesirable implications and incentives, to allow AIC's—from hiding liability to incentivizing loss of control. Surprised to see so many advocate for this...
Deeply concerning to me... Delaware proposal to let agents run businesees... we should be very cautious here "A Delaware AIC will be a separate legal entity whose day-to-day affairs are managed by an AI agent rather than a person. At the agent’s direction, the AIC can sue and be sued in its own name, hold and dispose of property, and incur obligations..." arstechnica.com/tech-policy/…
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Christopher Manning retweeted
It's been very exciting to see Perplexity Teammate in development. It's really a paradigm for the future, using AI for meaningful work, with persistence and continuity.
Today, we're launching Perplexity Teammates. They are personal agents for work. And change the paradigm from writing prompts in a session to giving high-level goals. Rolling out to Max users on the web today. Native apps (mobile, desktop) coming soon.
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Christopher Manning retweeted
What's the opposite of doomscrolling? Hopescrolling?
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Christopher Manning retweeted
I have friends who have been killed simply because they wanted the opportunity to vote in free and fair elections. So, Americans, please exercise the right you have to vote on Tuesday! Or today! #VOTE!
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Christopher Manning retweeted
What happens if you put Tversky similarity inside an RNN cell? I added a Tversky branch to mLSTM in Vision-LSTM and xLSTM. At equal params, half the depth matched or beat full depth, with ~20% fewer FLOPs and up to 2x faster training. Read the Article: arian32.medium.com/tversky-s…
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Christopher Manning retweeted
Synthetic data is used all the time now for LM pretraining...but can we carefully quantify the gains in compute efficiency and data efficiency?
Synthetic rewrites are now standard to improve data quality and when data is limited. But how much compute should you spend rewriting? And can a model trained on rewrites beat the rewriting model? We fit scaling laws to answer both: 1/🧵
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Great that @cityofpaloalto is building some new housing – but extremely little of it near the University Ave downtown transit and shopping hub, where it would be so useful 😿🌇 paloalto.gov/City-Hall/Housi…
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Congratulations to @aixventureshq on a great event. You are all so impressive… and it’s just the beginning! @RichardSocher @chrmanning @ellie Krish, Nick, Sebastian, Sara
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Christopher Manning retweeted
Meet Pika H3 Flex, debuting today as the #1 open weight model on DesignArena’s t2v board. It’s built for professional iteration, with exportable audio stems, resolutions up to 4k, and faster-than-realtime generation. It can produce a 15-second clip with soundtrack in as little as 7 seconds. So, yeah. It’s kind of a flex 💅
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Christopher Manning retweeted
Introducing Fern, Mirendil’s first model: pretrained from scratch, with up to 10M tokens of context! That’s ~0.5M lines of code (roughly Megatron-LM scale) or days of an agent’s work, all in context at once. Its first version, Fern 0.1, had a short round of post-training 🚢
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Shoutout to Christopher Manning (@chrmanning)! He founded the Stanford NLP Group, co-introduced GloVe word vectors, and taught CS224N nearly every year from 2000 to 2024. So many people learned NLP from his work. 🙌 aipeoplewiki.com/people/chri… #NLP #Stanford #AI
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Christopher Manning retweeted
Introducing Kimi K3.1 Pro: our most capable 3T-class open-weight model. 🔹 1M-token context, 2.8T total / 104B active params (MoE), Multimodal. 🔹 Full-project coding: games in Unreal, Unity & Godot. 🔹 Excels at parametric 3D modeling in Blender & CAD. 🔹 More reliable at finance work: Accurate modeling with hallucination down by 65%. 🔹 Matching the strongest proprietary models across coding, agentic and knowledge benchmarks. Kimi K3.1 Pro and K3.1 Pro Highspeed (2–3× faster) are now live on Kimi.ai, Kimi Work, Kimi Code, and the Kimi API. Open weights by Nov 4, 2026. 🔗 API: platform.kimi.ai 🔗 Tech blog: kimi.ai/blog/kimi-k3-1-pro
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