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The path to getting started as an AI researcher is pretty simple: read and build stuff. Scientific insights can come seemingly at random. An important trait for success is just putting in the time and effort. To become world-class takes a tremendous amount of discipline.
Google DeepMind examined how AI could progress beyond human-level AGI toward artificial superintelligence. The report outlined four possible pathways to ASI, potential bottlenecks, and the societal implications of continued acceleration in AI-driven progress.
Fable was special in ways that will not show up in benchmarks. It could perceive the user, infer intent, and think and iterate upon what it was given. The model felt alive. Mythos has changed the shape of the AI race. Other labs will likely eventually be able to replicate the magic of Mythos, but for many, the race is over.
Fireworks CEO Lin Qiao uses this week's Mythos shutdown to reframe the open-model conversation from cost to control, arguing a company built on intelligence it didn't own suddenly found itself exposed to decisions it couldn't influence. The playbook Fireworks ran with Ramp, Cursor, and Harvey already proves a tuned open model matches frontier quality at a fraction of the cost, but the deeper question is who owns the intelligence your product runs on.
General frontier models are the right starting point for 0-to-1 prototypes and understanding workflows, but for the handful of power-law use cases critical to a company's mission, product, and margin, the answer is increasingly to post-train your own model. Those use cases are where the differentiated data lives and where hard constraints on cost, latency, and reliability make a general model's single fixed tradeoff a liability.
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Shipping agents is only the beginning. LangSmith Engine helps teams detect production failures, generate fixes, and create evals that prevent regressions, enabling continuous improvement from traces
Sovereign AI is about how much of the supply chain required to train, operate, validate, and protect foundation models can be secured within one's own country or allied nations.
DocLang is an AI-friendly document format that helps enterprises feed their files to AI systems.
Codex Mobile lets developers start, direct, review, and organize work running on their development machines without pretending that a mobile device should be a tiny terminal.
Content owners and publishers can now set per-request pricing by content path, bot category, or verification tier without modifying their origin infrastructure or writing application code.
The GitHub Multilingual Repositories Dataset is a repository-level metadata dataset designed to help researchers and developers discover public GitHub repositories with evidence of non-English natural-language content.
Claims about AI GPUs having a lifespan of just three years stem from dubious sources like a tweet quoting an anonymous Google architect.
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Your harness runs any model on any cloud. You get context management, execution limits, and observability before you write a line of config.
Anthropic said it disabled Fable 5 and Mythos 5 for all users after receiving a US government export-control directive tied to national security concerns and reported jailbreak risks.
Conversations between Amazon chief executive Andy Jassy and US officials prompted the Trump administration to halt all foreign use of Anthropic's most capable AI models. Researchers at Amazon had used a series of prompts to get Anthropic's Fable 5 model to provide them with information that could be used to aid cyberattacks. White House officials asked Anthropic to fix the vulnerabilities or take down the model. Anthropic has shut down access to its Mythos and Fable models to comply, but says that the vulnerabilities flagged by Amazon are relatively basic and that other publicly available models are also capable of discovering them.
GLM-5.2, a new flagship model from Z.ai, is now available to all GLM Coding Plan users. It delivers powerful coding capabilities, usable 1M-context support, and continued strengths in long-horizon tasks. API and chatbot services will launch next week. The model will be open-sourced under the MIT License.
Google is integrating its products under Gemini Enterprise with a new "Skills Marketplace" tab for pre-defined, Google-optimized skills. The marketplace, aimed at helping teams develop dashboards and reporting tools without long engineering delays, includes a Skills Management UI, a Skills Builder, and the Marketplace itself.
You need the following information to work out the dollar price-per-user: GPU hardware specs, context length, active parameter count of the model, and product-specific factors. The specifics of the model architecture matter surprisingly little, unless it's something entirely different, like diffusion. This post shows how to work out the math on paper. The exercise should reveal how various optimizations in inference engines help SaaS products remain profitable.
Networks of smaller AI models are outperforming every frontier AI system on speed, accuracy, and cost. Everyone in the 1960s was wrong about the mainframe computer, and everyone is now wrong about centralized AI. The future is a network of neural networks.
Rafa Schwinger reverse-engineers Claude Mythos and Fable by arguing the moat is not architecture but the environment foundry, with capability decomposing as base foundation times gradeable signal extracted on top, and verifiable reward becoming the scarce decisive input now that text and raw compute no longer are. The recipe stacks dense pretraining, GRPO-style verifier RL where reward-hacking soundness is the actual binding constraint, long-horizon process rewards with learned context-folding that beats million-token windows at 32K active, plus best-of-N test-time compute exposed as an effort dial.
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Nearly two-thirds of tech leaders say their teams ship AI code without line-by-line verification. No wonder it keeps breaking in prod! Read New Relic's 2026 State of AI Coding Report [13] to see what engineers are doing with AI code, and what leaders think needs to
Kimi K2.7 Code is a coding-focused agentic model that has stronger end-to-end task completion across complex software engineering workflows and improved token efficiency compared to Kimi K2.6. The Mixture-of-Experts model has 1 trillion total parameters. It can be accessed on Moonshot's OpenAI/Anthropic compatible API. The model works best with Kimi Code CLI as its agent framework.
The Open Knowledge Format is an open specification that formalizes the LLM-wiki pattern into a portable, interoperable format. It is vendor-neutral and agent- and human-friendly. The standard can represent the metadata, context, and curated knowledge that modern AI systems need. The specification uses familiar patterns with no complex compression scheme, new runtime, or required SDK.
olmo-eval is a new evaluation workbench designed for iterative LLM development. Enhancing the OLMES standard, it streamlines adding benchmarks, supports agentic and multi-turn evaluations, and facilitates analysis by comparing changes across model checkpoints. Unlike Harbor, olmo-eval offers flexibility, minimizing resource use and focusing on development rather than public benchmarking.
MiniMax Sparse Attention is a sparse attention architecture that uses group-specific Top-k block selection to scale long-context inference while preserving model quality. On a 109B multimodal model, it matched GQA performance while cutting attention compute by ~30x at 1M tokens.
Object counting is still fragmented across domain-specific data sets and task formulations. Existing counting models are often tailored to specific scenarios and struggle to generalize across categories, visual domains, object scales, and density distributions. This paper presents a generalist model for text-guided object counting that achieves strong accuracy and multi-domain generalization.
We're hiring a senior PMM to own product marketing at TLDR. You'll define our positioning, build out sales enablement, and lead every launch. Learn more [20].
OpenAI and Anthropic have decided on different approaches to context management. OpenAI uses compaction, compressing everything and retaining only the relevant information. This results in one long thread that maintains a lot of coherence. Anthropic splits context windows across various agents, where each agent executes on the sub-problem within its own context window. Sub-agents do a large amount of work, then pass back only the relevant information to the parent agent. Anthropic's approach can result in sub-agents doing duplicate work, forgetting information, and generally wasting more tokens.
The iOS 27 beta contains an Extensions system for third-party AI. The system includes a settings panel and a dedicated App Store section. Both have been built, but are toggled off in the backend. Apple was in discussions with major AI providers about granting entitlements for the framework, but it appears the company has decided against announcing the feature for now.
Devin's engineering system guarantees more output than cost, with $10M pledged per customer. The system's effectiveness is validated using independent data. This bold claim aims to bolster confidence in their engineering productivity.
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The 2025 DORA report [24], featuring Nathen Harvey (Google Cloud) and Justin Reock (DX), cuts through the 10x AI hype to show what actually moves the needle for engineering teams and why culture and tooling
NVIDIA's Blackwell Ultra NVL72 platform leads the AgentPerf benchmark by delivering 20x more agent throughput per megawatt than NVIDIA Hopper.
Ramp released its own private SWE-Bench built from real engineering problems faced at Ramp, giving the team a way to evaluate coding models inside its actual financial software ecosystem.
Microsoft CEO Satya Nadella argues every company needs to build both human capital and token capital.
This thread explores a theory that DeepSeek's focus on open-source models, research sharing, and infrastructure development is part of a broader strategy aimed at becoming foundational AI infrastructure rather than competing directly on consumer products.
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For use cases like B2C ecommerce, AI search is a non-negotiable part of a modern UX. Vibe coding makes the basic search experience easy to build - but difficult to maintain and validate.
OpenAI announced it would acquire Ona to bring secure cloud execution and orchestration capabilities into the Codex platform. The technology is intended to support persistent, customer-controlled environments where agents can continue working across extended periods and sessions.
Anthropic has decided to make its safeguards for frontier LLM development visible after backlash from researchers. The company had previously discreetly rerouted requests to a lesser model when asked to perform certain actions. Researchers found that Claude Fable 5 was either refusing or degrading responses for tasks like training competing models, debugging AI code, and optimizing neural architecture. This raised concerns about Anthropic's lack of transparency and also that tokens and money had been spent on a model that didn't do what was expected.
Frontier AI models are typically trained on sequences of integers known as tokens. Each token refers to some sequence of bytes, and these byte sequences often correspond to common words. This post presents an algorithm that can compute an optimal tokenizer in some settings.
CoreWeave's co-founder, Brannin McBee, recently claimed that compute isn't fungible the way a commodity has to be. He has a real argument, but the non-commodity framing is the keystone of his company's value. While he appears to be saying that there is no market, he's actually pricing the market and revealing where the spread still hides.
This post shares how a developer created their own LLM from scratch. It covers how they create their own base-training and fine-tuning scripts, data processing pipelines, and custom dataset. The total cost of the project was around $80, but they had a decent PC to process the data. The model and code are available in the post.
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AI models are brilliant, but they're probabilistic. Without the ground truth of your operational reality, trusting AI is like trusting a fortune cookie. The Celonis Context Model [13] is a dynamic digital twin of your business that gives AI agents the operational clarity
MiMo Code V0.1.0 is an open source terminal-native AI coding assistant from Xiaomi. It outperforms Claude Code on key agentic coding benchmarks, particularly on long-horizon, multi-step tasks. MiMo Code features a cross-session memory system that uses an independent subagent to take notes of decisions, issues, and the scope of the project as it progresses. The model is available on GitHub under an MIT license.
This deep dive explores how PyTorch executes and optimizes neural network layers, progressing from individual linear operations to a fused multilayer perceptron.