Archive
The AI tool stack a developer uses in 2026 looks nothing like it did eighteen months ago. Agentic editors, review tools, terminal agents, and test assistants have settled into recognizable roles. A practical guide by category.
By late 2025, 57.3 percent of organizations had agents in production, up from 51 percent a year earlier, according to LangChain's survey of more than 1,300 professionals. Three failure modes dominate the postmortems: degenerative reasoning loops, hallucinated data in RAG systems, and silent misalignment between the request and the interpretation.
Twenty months after the initial announcement, Model Context Protocol went from curiosity to de-facto standard among agent clients and servers. What is available, which servers are worth it, which problems remain open, and how it compares to earlier protocol maps.
Humanoid robotics left the trade-show floor for factory floors and warehouses during 2025 and 2026. Which companies have really deployed units, which tasks fit, what real costs look like, and where humans remain unbeatable.
NPUs stopped being an accessory and became the component that defines real performance in laptops, phones, and small servers. A practical look at the hardware that rules 2026, which workloads pay off, and where the traditional GPU still wins.
The European AI Act took effect on 1 August 2024 with a staggered calendar, and its Annex III high-risk rules no longer land in August 2026. The Digital Omnibus, closed by the Parliament and the Council, moves that deadline 17 months to 2 December 2027. Prohibitions since February 2025 and general-purpose AI duties since August 2025 still apply.
Platform engineering worked where teams built on concrete, painful problems and offered golden paths developers actually wanted, run with a product mindset. It stalled where the output was an empty Backstage portal: technically correct, unvisited, solving no operational problem. Three years after the Gartner hype of 2023, that split separates the winners from the sunk cost.
Four and a half years after Rust officially entered Linux 6.1, with real Apple GPU and NVMe drivers in production and several public conflicts between maintainers, it is time for a sober technical balance. What works, what still costs, and where the next phase is heading.
FinOps for AI counts different units than classic cloud FinOps: tokens, calls, computed embeddings and GPU time, all of which scale nonlinearly with use. The costliest habit is sending everything to frontier models; 40 to 70 percent of those calls run on mid-tier models with no noticeable quality loss. Uncached RAG and self-recursing agents do the rest.
Sixteen months after Anthropic first shipped computer use, with browser-use, OpenAI Operator and Gemini Computer Use all pushing in parallel, agents that drive the browser and desktop have moved from demo to real workflows. Time to review which patterns survive when you run them daily in production.
