What if every decode step gave the next one a head start? Meet Guess-Verify-Refine — a new hardware-aware sparse-attention algorithm from NVIDIA Research. Built for TensorRT LLM on Blackwell, it reuses temporal patterns across decode steps for: → 1.88x faster Top-K attention → 9.3% better end-to-end latency in low-latency serving Dive into the paper:

Right now, you're probably: → Managing API keys for every LLM provider → Rewriting code every time a new model launches → Hoping your pipeline survives the next outage What if one API handled it all? 😎 Claude. GPT. Gemini. Grok. DeepSeek. Plus 200+ more models.👇

We’re rolling out changes to make Fable 5’s safeguards for frontier LLM development visible. Starting this week, flagged requests will visibly fall back to Opus 4.8—the same as our safeguards for cyber and bio. You will see this every time it happens. On the API, any flagged requests will return a reason for their refusal (coming to server-side fallback in the next few days). We wanted to deploy Fable 5 to our users quickly and safely. Visible safeguards can be probed, so they have to be robust, which takes time to get right. Invisible safeguards can be targeted more narrowly, allowing us to ship quickly with very few false positives. We went with invisible safeguards for this reason—and that was the wrong tradeoff. You should have visibility into the safeguards we have in place, and why. We’re sorry for not getting the balance right. Making the safeguards visible makes them easier to work around, so keeping them robust to jailbreaks will unfortunately mean more false positives while we improve the classifiers. We're also tuning our bio and cyber classifiers to trigger less often on harmless requests. We know this is frustrating and we’ll do our best to keep this period as short as possible. If you think a request has been mistakenly flagged: run /feedback in Claude Code, click thumbs-down on the fallback in or Cowork, or file the safeguard appeal form for API requests. Your reports help us tune these classifiers and we appreciate your feedback.

A useful but little-known OpenAI API feature 📦 Sending thousands of LLM requests one by one and wincing at the bill? There's a much cheaper way. OpenAI's "Batch" API lets you bundle requests together for async execution at a significant discount. It's the go-to for evaluations, classification, data generation, and any high-volume job that doesn't need real-time responses. 📌 Title: Batch 🔗 URL: 🧩 Overview When you're calling the LLM at scale, sending requests one at a time is expensive and slow. The Batch API lets you upload requests as a JSONL file, process them all asynchronously, and get results at a steep discount compared to standard API calls. Results are collected once the batch completes. 🛠 How to use it Compile your requests into a JSONL file, upload it, and create a batch job. When processing finishes, download the results file. Each request uses the same Chat Completions format you already know, so existing prompts work as-is. Pair with Webhooks to get notified automatically when a batch is done. 🏗 Building it into production ・Dataset classification and labeling: run tens of thousands of text categorizations as an overnight batch. Labeled data is ready by morning. ・Synthetic data generation pipelines: when you're generating training data at scale, the batch discount makes a material difference to your bill. ・Model evaluation and benchmarking: run quality comparisons across multiple prompts in one shot. Analyze results together. ・Periodic summarization and reporting: weekly article digests, customer feedback analysis, anything that processes in bulk on a schedule. 💡 Use cases 🗂 Large-scale text classification and tagging 🧬 Synthetic and training data generation 📊 Model evaluation and prompt comparison 📝 Scheduled batch summarization and extraction ⚠️ Watch out Batch processing is async, so results take time to come back. Not suitable for anything that needs a real-time response. Individual requests within a batch can also fail, so build proper error handling when parsing the results file. Start with a small test batch before submitting massive jobs. ✨ The foundation of cost optimization at scale is batching. Switch your evaluation pipeline to Batch first and see the difference on your next invoice. #OpenAI# #LLM#

A useful but little-known OpenAI API feature 📦 Sending thousands of LLM requests one by one and wincing at the bill? There's a much cheaper way. OpenAI's "Batch" API lets you bundle requests together for async execution at a significant discount. It's the go-to for evaluations, classification, data generation, and any high-volume job that doesn't need real-time responses. 📌 Title: Batch 🔗 URL: 🧩 Overview When you're calling the LLM at scale, sending requests one at a time is expensive and slow. The Batch API lets you upload requests as a JSONL file, process them all asynchronously, and get results at a steep discount compared to standard API calls. Results are collected once the batch completes. 🛠 How to use it Compile your requests into a JSONL file, upload it, and create a batch job. When processing finishes, download the results file. Each request uses the same Chat Completions format you already know, so existing prompts work as-is. Pair with Webhooks to get notified automatically when a batch is done. 🏗 Building it into production ・Dataset classification and labeling: run tens of thousands of text categorizations as an overnight batch. Labeled data is ready by morning. ・Synthetic data generation pipelines: when you're generating training data at scale, the batch discount makes a material difference to your bill. ・Model evaluation and benchmarking: run quality comparisons across multiple prompts in one shot. Analyze results together. ・Periodic summarization and reporting: weekly article digests, customer feedback analysis, anything that processes in bulk on a schedule. 💡 Use cases 🗂 Large-scale text classification and tagging 🧬 Synthetic and training data generation 📊 Model evaluation and prompt comparison 📝 Scheduled batch summarization and extraction ⚠️ Watch out Batch processing is async, so results take time to come back. Not suitable for anything that needs a real-time response. Individual requests within a batch can also fail, so build proper error handling when parsing the results file. Start with a small test batch before submitting massive jobs. ✨ The foundation of cost optimization at scale is batching. Switch your evaluation pipeline to Batch first and see the difference on your next invoice. #OpenAI# #LLM#

🤔 "Building an AI agent isn't hard. But keeping it running in production? That's an entirely different challenge." — Every agent developer has hit this wall. Why Managed Agents Are the Next Big Thing in Agent Building Since LangChain launched in late 2022, agent development has evolved at a breathtaking pace. From the early excitement of AutoGPT, through the era of precise frameworks like LangGraph and Google ADK — and then in 2025, as models crossed a capability threshold, the fundamental pattern of "an LLM autonomously looping while calling tools" finally became real. The emergence of dedicated harnesses like Claude Code and Deep Agents marked another step toward the finished form. 🌊 But a deeper problem surfaced: sheer model intelligence isn't enough to keep agents running reliably in production. How do you build a trustworthy runtime? How do you resume a failed agent mid-run? How do you sandbox code safely? How do you design the UX for end users? Developers found themselves wrestling with infrastructure challenges that had nothing to do with the model itself. LangChain founder Harrison Chase crystallizes a year of hard-won lessons into the concept of "managed agents." He frames production agents as a three-layer stack — business logic, harness, and infrastructure — and positions Managed Deep Agents as the platform that takes ownership of that infrastructure layer. Runtime management, event streaming, sandboxes, memory, authentication, evaluation tools: everything developers once had to build themselves is now offered as a managed service. Anthropic's Claude Managed Agents and Vercel's Eve are converging on the same vision, signaling that the entire ecosystem is moving toward democratizing infrastructure. The era when agent developers can focus purely on business logic is finally within reach. #AIAgents# #LangChain#

65 million monthly downloads. LangGraph just shared what three years of graph engineering actually taught them. Title: 3 Years of Graph Engineering with LangGraph The core idea of modeling agents as graphs: not handing control to the LLM, but letting developers embed expected behavior flows as constrained paths. Nodes run computation; edges define what happens next — giving you precise control over the balance between deterministic code and autonomous steps. 🔄 Highlight 1 — Agent graphs are NOT DAGs The biggest trap is assuming you can design everything as a directed acyclic graph. In production, you always need cycles: retrying failed tool calls, asking users for missing information, correcting answers after validation failures, resuming after human checkpoints. Loop engineering isn't an alternative to graphs — it's just a simpler special case. LangChain itself is built as a simple loop on top of LangGraph. 🧩 Highlight 2 — Full agent runs can live inside a single node The biggest evolution over three years: what you can put inside a node. Early on, nodes held deterministic code or single LLM calls. Now, entire agent executions fit inside one node. A Slack-to-pull-request system illustrates this: deterministic API calls, a simple classifier, and an autonomous codebase-exploring agent all coexist in one graph — achieving predictability, power, and efficiency together. 📤 Highlight 3 — Send API enables dynamic routing Map-reduce workflows can't have all edges defined upfront because node output volume is only known at runtime. The Send API routes work dynamically to multiple downstream nodes, breaking this constraint. The post also draws a clear boundary: for deep research tasks where the flow can't be predetermined, reach for an agent harness instead of a graph. Graph engineering isn't a new idea — it's the latest expression of the same lineage as loop engineering and harness engineering. #LangGraph# #AIAgent#

In this GTC session, Bill Dally (Chief Scientist and SVP of Research, NVIDIA) and Jeff Dean (Chief Scientist, Google DeepMind and Google Research) discuss how LLM in the engineering workflow is changing how GPU teams ramp talent and share knowledge. Watch Advancing to AI's Next Frontier today.

"Sam was early, but perhaps right - the combination of the model with compute is the moat". When I first met Sam and spent some time with him, he would talk about building GWs of capacity and at every next meeting his ambition for compute got higher - to a point where I started half listening, since I was thinking of all the other repercussions. The raging debate is where is the value being created perhaps where will it be captured. We are quick to write off models and are debating open weight and open source, worthy debates but the action is elsewhere. The infrastructure layer is the current beneficiary of spend, and appropriately the innovation engine of silicon valley is targeting chips, inference, memory and anything that can satisfy the voracious demand for infrastructure spend. But it's the people building compute who are doing the spending here. The neo clouds have raced ahead and and credit to them for seeing the trend early - they were able to grab early permits, land and power to be able to deploy compute. It continues to become harder to deploy compute as the demand curve goes vertical. The hyperscaler earnings have shown that they don't intend to relinquish their infrastructure advantage and are continuing to increase capex to both satisfy and anticipate future demand. Anyone not there yet, is probably concerned about raising funds and not the viability of the spend. The Frontier LLM players are into the act and are working hard to aggregate compute since they know they will need more not just to train but to deploy. The race is on, for the Frontier models to win, they will need to look more like hyperscalers with a twist, how to embed large application layer profit pools into their business model. You can see it happening. coding, science, legal, and soon physical AI. At this scale timing will become an important vector, including the financial markets, hope much patient capital is out there. The market likes to surprise.

If you're a king, queen, president, prime minister, emir, sultan, or any leader of a country, make the development of innovative LLM infrastructure, AI algorithms, and supporting hardware TAX-FREE for at least the next decade. Not for companies that simply use AI, but for those building better AI models and the infrastructure behind them. Provide incentives to bring back expat talent. If you do, your country might have a chance to survive what’s coming.