14 results
AI tools change more than your speed — they change how you think. The shift from 'how to do it' to 'what to do' and 'is this right?' has real long-term implications for engineers.
OpenAI Codex CLI and multiple AI coding agents have free tiers. The key is understanding each tool's quota mechanism, how to combine them to extend free usage, and when paid tiers are actually worth it.
Build a video production AI Agent with LangGraph that handles research, scripting, and storyboarding — the key is state machine design and conditional edges for error handling.
AI agent billing spikes come from three places: using a stronger model than the task requires, no depth limit on tool call loops, and context window waste from passing full history every round. The correct cost control strategy is matching model capability to task complexity, not using the strongest model for everything.
DeepSeek V4 is a 1.6T parameter MoE open-source model with 1M token context that claims to outperform GPT-5.2 on some benchmarks — and is DeepSeek's first model optimized for Huawei Ascend chips.
AI Agents let models perceive environments and act autonomously. Harness Engineering is the discipline that makes them reliable — the scaffolding that turns a smart-but-unpredictable model into a deployable engineering system.
Built an LLM-powered bot that explains anything with condescending overconfidence. 90% of the engineering went into system prompt design, not code.
Python is still the dominant language for AI development, but the rise of AI coding tools is blurring the line between 'writing Python code' and 'doing AI development' — this is what that shift actually means.
KV Cache reduces autoregressive Transformer generation from O(n²) — recomputing the full sequence for every new token — to O(n) per step, which is the core reason modern LLM inference is fast enough to be usable.
DeepSeek V3's 671B-parameter MoE architecture trained on just 2.78M H800 GPU-hours matches near-GPT-4 performance across multiple benchmarks, with API pricing at one-tenth of OpenAI's equivalent.
OpenAI released three models in spring 2025: GPT-4.1 for coding and instruction-following, o3 as the strongest reasoning model, and o4-mini hitting remarkable math and code performance at low cost — but the pricing strategy and API access limits left developers with mixed feelings.
AI agents degrading over long sessions isn't a model problem — it's a context problem. As the context window fills with failed attempts, outdated code, and contradictory instructions, signal-to-noise ratio drops. The fix is treating context like RAM, not a filing cabinet.
LLM output quality is determined at three distinct layers: token-level decoding strategy, task-level workflow design, and model-level reasoning capability. Knowing which layer your problem lives in is the fastest path to fixing it.
Small language models around 10B parameters can run on local hardware in real time, enabling dynamic NPC dialogue, procedural narrative generation, and adaptive game content. Research shows SLMs approach large model quality on short, well-constrained creative tasks — the key is curated training data and constrained inference design.