Memory is what separates a useful AI agent from an expensive autocomplete tool. Without memory, an agent forgets every conversation the moment it ends, cannot learn from past interactions, and cannot build understanding of a user or task across sessions. With the right memory architecture, an agent accumulates context that makes it progressively more useful … Read more
Adding web browsing to an AI agent lets it retrieve current information, navigate websites, fill forms, and extract data from live pages — capabilities that static language model knowledge cannot provide. A browsing-capable agent can check today’s news, look up a product price, read a documentation page, or scrape a table of data in real … Read more
Qwen QwQ-32B is Alibaba’s open-weights reasoning model — a 32-billion-parameter model that matches or exceeds the performance of much larger models on mathematics, coding, and logical reasoning benchmarks by using extended chain-of-thought reasoning at inference time. Running it locally with Ollama takes about five minutes and gives you a capable reasoning model that runs entirely … Read more
DeepSeek-R2 is the successor to DeepSeek R1, released in mid-2026, and it maintains the trend R1 established: open-weights reasoning models that match or exceed proprietary frontier performance at dramatically lower training cost. R2 represents a meaningful step beyond R1 on most benchmarks, and its release continued the pattern of the open-weights ecosystem closing the gap … Read more
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
