AI agent frameworks are libraries and platforms for building systems where language models take sequences of actions — using tools, browsing the web, writing and executing code, managing files, calling APIs — to accomplish goals that require more than a single prompt-response cycle. The frameworks vary widely in philosophy: some are minimal orchestration layers, some … Read more
Mathematics is the domain where reasoning models most clearly outperform standard language models. On competition-level benchmarks, the performance gap between reasoning models and standard models is larger than in almost any other domain — reasoning models score 70–95% on problems that defeat standard models at 20–40%. This is not a coincidence: mathematics is precisely the … Read more
LangGraph, CrewAI, and AutoGen are the three most widely compared agent frameworks as of 2026. Each has a distinct philosophy, a different primary use case, and a different trade-off between control and convenience. Choosing the wrong one for your architecture is not catastrophic — you can migrate — but it costs time and creates technical … Read more
Prompting reasoning models effectively requires a different mental model than prompting standard language models. The core difference: reasoning models do their own thinking, so your job is to give them a clear problem and the right output format — not to prescribe a reasoning process. Prompts that work well with standard models (step-by-step instructions, chain-of-thought … Read more
DeepSeek released R1 alongside a family of distilled smaller models — ranging from 1.5B to 70B parameters — specifically so practitioners could run powerful reasoning locally without a data centre. The choice between 14B, 32B, and 70B is not simply “bigger is better”: it involves your hardware, your tolerance for latency, and how much of … Read more
Test-time compute scaling is the observation that spending more compute during inference — having a model think longer rather than answer immediately — improves output quality in ways that parallel the improvements from spending more compute during training. It is the key insight behind reasoning models: instead of making the model larger or training it … Read more
Budget forcing is a technique for constraining how much a reasoning model thinks before answering. It works by explicitly limiting the number of thinking tokens the model can use — either by setting a hard token cap, by injecting a prompt at a specific point in the thinking trace, or by using a model API’s … Read more
Reasoning models perform substantially better than standard language models on hard coding tasks. On SWE-bench Verified — a benchmark that evaluates models on real GitHub issues from major open-source repositories, where the model must write code changes that make tests pass — the performance gap is striking. Standard GPT-4o class models score around 33–38%; o3 … Read more
When DeepSeek released R1 in January 2025, it was the first open-weights reasoning model to match OpenAI o1’s benchmark performance on mathematics and coding — and it arrived at a fraction of the training cost, with weights anyone could download and run locally. The comparison between DeepSeek R1 and OpenAI o1 is significant not just … Read more
OpenAI’s o-series models replaced the traditional “scale up the base model” approach with an explicit reasoning step: the model thinks through a problem before producing an answer, spending more compute at inference time rather than training time. o3 and o4-mini are the current generation of this series. They differ not just in size but in … Read more
