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ne was designed with strict quality and activity thresholds to ensure that only mature, collaboratively developed projects were included Together, these pipelines ensure broad coverage across package ecosystems, popular open-source projects, and rich multimodal codebases, resulting in a balanced and rigorously filtered dataset suitable for downstream analysis and model training. Below is a quick reference to the key fields captured for each dataset instance. Code Execution Platform Overivew This

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new tools. It means developing the judgment to use those tools well. Our hiring process should help us identify engineers who can combine technical depth with that new kind of leverage. " Hagay Lupesko SVP of Engineering Here is what changed and what we have learned so far. Instead of centering an interview on an isolated algorithm, we added exercises that resemble the work engineers encounter after joining a team. A candidate might explore a small repository, add a capability to an existing ser

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y quirks. When migrating to GLM 4.7, a common mistake is reusing old prompts without adjusting them for its unique behavior, which can lead to suboptimal performance. To fully leverage GLM 4.7's strengths, it's essential to refine your prompts, architecture, and sampling parameters accordingly. Below are 10 rules to help you get the most from GLM 4.7. On Cerebras, GLM 4.7 supports up to 131K context length. However, like most other large models, the output quality for GLM4.7 is most accurate at

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n system, I ask it to interview me first. Especially with fast models, this type of back-and-forth iteration and conversation can be done in seconds. It can also be helpful to leverage the grill-me skill. This works well because the model is often better at noticing what’s missing than I am. It will ask things like: Thomas Wiegold’s writeup on frontend-design plugin makes a similar point when it comes to comprehensive style guides: instead of relying on magic tooling, it’s better to force the mo

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ed. To draw the outlines on the images, we used Gemma 4’s structured outputs to generate the bounding boxes over the images using Javascript. Each batch of three images took an average of 300ms to process. All 17 frames were processed and annotated in just 5 seconds, start to finish. The full side-by-side demo makes the latency difference obvious. Watch on as we run it side-by-side against a popular GPU provider serving the same Gemma 4 31B model. The results speak for themselves. Same request,

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how to use the MCP. The main issues I ran into: - Figma designs taking up too much context - The agents not knowing how to use the updated tool out the box - Long run-times (task drags out for hours) Here’s how I solved them: - Using subagents to ensure we don’t overwhelm a single agent - Looking up skills and guides on Figma’s Github - Parallelise work I asked Codex to go through all the past sessions, figure out the failure points, then search the web on what the proper guidelines would be. 1.

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atforms. Visual tools for building AI applications without extensive custom code (Dataiku, DataRobot, Dify, Flowise, FlutterFlow, StackAI). These tools are particularly useful if drag-and-drop AI application development is preferred. Observability and evaluation. Tooling for tracing, evaluation, monitoring, and traffic management in production AI systems (Arize Phoenix, Braintrust, Cloudflare AI Gateway, Helicone, Kong, Langfuse, Operant, Opik, Weave). Solution providers. Channels that help orga

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generation. To get the most out of Spark, start fresh sessions often and keep your research context inside your repo. Treat the context window as working memory, not long-term storage. Keep durable state in files. Keep sessions focused. Reset when goals are complete. A useful mental model is: The best trick for managing context length: break larger tasks into small, bounded goals. Prompt each session to make incremental progress toward one goal, so that every session produces bug-free, well-doc

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han Varma Product at OpenAI Ultrafast is a persistent edge for organizations using frontier AI to quickly respond to incoming information. Companies operating web services can leverage Ultrafast to root-cause and address production outages, preserving customer trust, preventing lost revenue, and saving downtime minutes against their SLAs. And in adversarial, high stakes cyberattacks, Ultrafast is an invaluable tool for security teams who must quickly detect and respond to bad actors to contain c

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del Rocio Martin Rodriguez Graduate Student School of Humanities & Sciences Linguistics The Byte to Bite affinity group will focus on how the computational power of AI can be leveraged to help people make healthier, more sustainable, community-building choices around food. Through personal nutrition, dietary restrictions, location, cultural background, and more, everyone’s definition of healthy eating, and path to get there, is vastly different. AI has become a powerhouse for absorbing these co

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A new Stanford-led study finds that X’s “For You” algorithm mistakes outrage for interest – and fills your feed accordingly. In their “For You” pages, social media algorithms purport to find you exactly the kinds of content you want to see and gather it together in one easy-to-peruse section. That is, if you click on and watch enough political videos, you should expect lots of politics in your algorithmically curated feed. But a new study finds a curious phenomenon: The smarter the algorithm get

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ual safety gaps. It’s really a much more detailed look at the many facets that comprise safety. Measurement science helps us get inside this complexity. What’s at stake for the average person? Truong: We argue that benchmarks are quite consequential to the future of AI. A model’s ranking on a performance leaderboard shapes its market value. It drives financial decisions by developers and investors, and it influences regulations by policymakers. Because benchmarks are key to data-driven, scientif

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ve introduced more than 140 bills related to AI use in mental health contexts. Federal legislation is pending , but so far has been narrowly focused on protection of minors. This fragmented policy landscape is further hindered by a perpetually lagging evidence base : Many purpose-built AI mental health tools lack validated outcomes and representative samples, and are rarely evaluated with rigorous study designs. Meanwhile, the models powering general-purpose chatbots update so rapidly that safet

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row calls “enshittification.” “A closed frontier model behind an API is the ultimate walled garden,” Landay said. “You don’t own your data, your history with the model, or any leverage to leave. That’s already the norm.” The longer someone stays inside one of those gardens, he added, the more of their own context and history the platform accumulates – which only raises the cost of ever leaving, deepening the lock-in rather than easing it over time. There’s a security argument here too: Relying s

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nuclear proliferation in large-scale satellite imagery dataset, a volume of data that would be prohibitively time consuming to analyze by hand. Now we have the opportunity to leverage multimodal data including multilingual text, online media, and satellite imagery, to design AI protocols to help analysts detect illicit procurement of nuclear proliferation and undeclared nuclear infrastructure. A multidisciplinary team of computer scientists, imagery analysts, and open-source nonproliferation re

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view lines the same questions up across two model checkpoints and compares them one by one, with all else held fixed. This helps you to see whether a tiny change in an overall average might indicate a real improvement or simply noise. An integrated evaluation stack olmo-eval is composed of four components that are useful on their own but designed to work together to tighten the experimental LLM development loop: A task/suite/harness abstraction that decouples benchmark logic from runtime policy

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token better; a negative gap means the transformer did. To find where the loss gaps might concentrate, we ran several analyses. First, we sorted each token into a category and averaged the loss gap within these categories. Because a raw average can be skewed by other factors, such as a category's rarity or how often tokens repeat in a sample of text, we re-checked each pattern with a regression that estimates the category's own effect while holding other factors constant. We find that Olmo Hybr

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valuate density and score at any point—not just where you have data. Score and density share a mathematical relationship: score is the gradient of the logarithm of density. We leverage this by having a shared backbone with two output heads, one for the density and one for the score. This coupling does more than save parameters. The score head has to match the gradient of the log-density head at every query, so any gap between them is a label-free consistency loss. We use this at inference—hold t

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h Platform, with statistics. Why satellite inference is challenging Most ML models take in a few megabytes of data and produce a result in under a second—think LLMs processing a paragraph of text or computer vision models analyzing a photo from a smartphone. Earth observation inference operates at a very different scale—a single job fine-tuning a foundation model for maximum performance can move terabytes of data and run for hours. The inputs may span multiple spectral bands, sensor types, and t

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od Research is another research organization that assesses AI agent risks and containment measures. ↩ OpenAI, The Hugging Face incident and the road ahead , section ‘Safeguard coverage in internal evaluations’. This is not an uncommon practice within frontier AI agent testing. Blocking agent actions too aggressively can prevent understanding the upper limits of what the agent can do. ↩ This was not the first time OpenAI agents created an unsanctioned “message board.” OpenAI’s report documents si

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