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raw algorithmic operation requirements by aggregate arithmetic bandwidth. This is catnip for engineers. It’s also especially critical for “hero run” large-scale training, so it draws a lot of investment and attention, e.g. recently as everyone dunked on xAI’s 10% MFU. But at the other end of the stack, there’s a more basic form of utilization that wrecks the relationship between achieved output and allocated capacity for inference workloads, GPU Allocation Utilization: GPU Allocation Utilizatio

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run carrying the task ID and execution config. No ordering, no deduplication, no separate queue per workload type since agent tasks are independent of each other. The worker is an AWS Lambda function with from browser use import Agent at the top. It pulls messages off SQS, instantiates the agent, runs it to completion, and writes results to S3. The complexity is in how each piece handles failure. API entry The handler validates the payload, creates session and task rows in the database, commits,

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reimplementing all of the features of Slay the Spire and Balatro. The first run for both agents didn’t yield an impressive result – the Slay the Spire implementation had obvious flaws around game progression, and the Balatro implementation had a broken user interface that made it unplayable. Appendix: Token Usage Table 1: Token usage for the Slay the Spire run. Table 2: Token usage for the Balatro run. The token usage adds up to around $20 for the Slay the Spire run, and around $6 for the Balatr

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prompt and settings are in our open-source agent at github.com/browser-use/browser-use. We are moving toward a fully closed loop: an automated infinite self-improvement cycle where the agent evaluates itself, finds its own flaws, writes its own patches, and proves its success statistically. To see how these models actually compare, check out our definitive model benchmark. For the full technical report on our earlier WebVoyager results, see our SOTA technical report. Published February 23, 2026

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ation in practice: when prompted to both generate and analyze its own proof in one shot, the generator tends to claim correctness even when the external verifier easily identify flaws. In other words, while the generator can refine proofs based on external feedback, it fails to evaluate its own work with the same rigor as the dedicated verifier. As a logical consequence, one would assume they use a separate proof generator (LLM 1) and proof verifier (LLM 2). So, the self-refinement loop used her

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so, if efficiency is not the main factor, the best we have for now. Traditional Decoder-Style, Autoregressive Transformers + Proven & mature tooling + “well-understood” + Scaling laws + SOTA - Expensive training - Expensive inference (except for aforementioned tricks) If I were to start a new LLM-based project today, autoregressive transformer-based LLMs would be my first choice. I definitely find the upcoming attention hybrids very promising, which are especially interesting when working with l

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ine and distributed serving stack—closing with how to measure system performance. vLLM also includes specialized handling that I've skipped. E.g.: Diverse hardware backends: TPUs, AWS Neuron (Trainium/Inferentia), etc; Architectures/techniques: MLA, MoE, encoder-decoder (e.g., Whisper), pooling/embedding models, EPLB, m-RoPE, LoRA, ALiBi, attention-free variants, sliding-window attention, multimodal LMs, and state-space models (e.g., Mamba/Mamba-2, Jamba); TP/PP/SP; Hybrid KV-cache logic (Jenga)

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The future of Triage Intelligence⁠ From here we intend to move in the direction of more automation and decisions based on richer context. Triage Intelligence will improve as it draws on a deeper understanding of your workspace and as we adopt newer models and techniques. With that expanded context, it will be able to deliver more accurate suggestions and handle a wider range of scenarios. We’re adding new capabilities outside of just triage support, including using LLMs to draft project and ini

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g Short-Term Memory. Neural Computation; Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., & Amodei, D. (2020). Scaling Laws for Neural Language Models. arXiv preprint arXiv:2001.08361; Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781; Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in t

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is usually enough. When and how to use frameworks There are many frameworks that make agentic systems easier to implement, including: The Claude Agent SDK ; Strands Agents SDK by AWS ; Rivet , a drag and drop GUI LLM workflow builder; and Vellum , another GUI tool for building and testing complex workflows. We suggest that developers start by using LLM APIs directly: many patterns can be implemented in a few lines of code. If you do use a framework, ensure you understand the underlying code. In

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. This indicates a potential problem with using LLM to evaluate its own performance on domains that requires deep expertise. The lack of expertise may cause LLMs not knowing its flaws and thus cannot well judge the correctness of task results. Boiko et al. (2023) also looked into LLM-empowered agents for scientific discovery, to handle autonomous design, planning, and performance of complex scientific experiments. This agent can use tools to browse the Internet, read documentation, execute code,

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