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Most agent demos require an API key, break offline, and have no real guardrails. That's fine for a toy, but it fails quickly in real engineering contexts where latency control, reliability guarantees, and enforceable policies matter. The goal was to build a system that could demo offline, with
This week underscored the AI industry’s twin realities: escalating geopolitical tension and unprecedented capital scale. While Anthropic’s clash with the Pentagon highlighted the growing friction between AI safety commitments and government power, OpenAI’s $110B raise showed just how enormous the frontier compute race has become. At the
Reviews
AI video generators are often presented as a shortcut to cinematic, studio-quality content — tools that promise to turn a simple idea into something that looks ready for Hollywood. In this article, we take a grounded look at how that promise holds up in practice. Rather than digging into advanced settings
News
AI this week was defined by scale and risk: massive funding moves, faster specialised models, and platform-wide upgrades from major players like Google and OpenAI. At the same time, real-world incidents — outages, security bans, and governance debates — showed how quickly agentic systems are colliding with enterprise reality. Google ships Gemini
News
This week showed AI moving decisively into its commercial phase — massive funding rounds, ads entering chatbots, and enterprises pushing AI deeper into production workflows. At the same time, the tech stack is shifting fast, with new ultra-fast coding models, hardware plays beyond NVIDIA, and rapid model retirements signalling how quickly
Comics
Comics
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Big moves in AI agents, coding tools, and enterprise platforms as OpenAI and Anthropic escalate competition, regulators step in, and media, infrastructure, and security grapple with AI’s growing real-world impact. OpenAI introduces Frontier, an enterprise platform to build/manage AI agents OpenAI launched Frontier to help enterprises deploy and
If you've spent any time with AI coding assistants, you've probably experienced the moment where everything falls apart...
Large language models are often judged on complex benchmarks, but some of their most interesting failures show up on questions that seem trivial at first glance. In this article, we test a range of OpenAI models using a small set of deliberately easy questions, the kind that have a track
Claude Opus 4.5 has been drawing attention for its coding skills, so I decided to put it to the test on a problem I’ve struggled with before: getting an AI to write shaders that actually work. I was curious to see whether it could handle the challenge better
In a recent development out of Samsung’s AI lab in Montreal, researchers have introduced a new “Tiny Recursive Model” (TRM) that challenges the prevailing notion that more parameters = more intelligence.) Key ideas & claims * Tiny footprint: TRM has only ~7 million parameters—orders of magnitude smaller than typical large
We often treat large language models as if they’re deterministic tools: ask the same question twice and you’ll get essentially the same answer. But anyone who uses them regularly knows that isn’t always true. Sometimes responses drift. Sometimes they change a lot. And sometimes they break your
