You open your editor, describe what you want, and a few seconds later there is code on the screen. It is not perfect, but it is usually good enough to keep moving — especially when you are exploring something new or trying to get to a first working version. That removes a lot of friction, […]
Coding Agents Feel Cheap. That Might Not Last. Weiterlesen »
Over the past year, the discussion in AI has gradually shifted away from models as isolated reasoning engines and toward agents as autonomous operational systems. Large language models are no longer framed merely as tools for generating text or answering questions. They are presented as components capable of planning, acting, coordinating across APIs, and making
Everyone Talks About Agents. Nobody Talks About State. Weiterlesen »
When you scroll through social media today, you might come away believing that OpenClaw has ushered in a new era of autonomous AI assistants that you can drop straight into production and have them “just work.” That impression is misleading. OpenClaw, formerly known as Clawdbot and Moltbot, is a clever and technically interesting side project
OpenClaw Is Not the Autonomy Revolution You Think It Is Weiterlesen »
Over the last couple of days, my LinkedIn feed has been flooded with euphoric posts about “Code AI” and “local coding assistants”. Screenshots of terminals, bold claims about productivity exploding, and the familiar undertone that if you are not running an LLM locally via Ollama, OpenCode, or Copilot, you are already falling behind. I know
Running Code AI Locally: An Engineering Reality Check Weiterlesen »
This project did not start as an attempt to build a generic image recognition system or to benchmark computer vision frameworks. It started with three teddy bears that have been traveling with me since 2017. Over the years, they have accompanied me on flights, through airports, into hotel rooms, conference venues, cafés, and occasionally onto
Teaching a Machine to Recognize Traveling Bears Weiterlesen »
There is a growing belief that software engineering has become an optional skill and a 20-dollar subscription with the right prompts can build complex systems without understanding architecture, versioning, security, or operational reality. Engineers, according to this narrative, are a bottleneck that can be removed. I am skeptical of claims like these, but I do
Vibe Coding: Why It Feels Productive and Why It Fails Engineering Weiterlesen »
Spend enough time in the AI space and you start to notice a pattern. There’s a lot of talk about modeling—neural architectures, parameter tuning, accuracy curves, and leaderboard rankings. And yet, when you actually try to bring an ML system into production, the modeling phase feels oddly… smooth. Controlled. Even pleasant. Because the real chaos?It
The Hardest Part of Machine Learning Isn’t the Machine Learning Weiterlesen »
In the landscape of AI and machine learning, Amazon Web Services (AWS) has introduced two major services—SageMaker and Bedrock—that cater to the needs of developers and businesses seeking to deploy machine learning (ML) models at scale. Although both services enable the integration of AI into various applications, their use cases and functionalities differ significantly, warranting
Navigating AWS SageMaker and Bedrock: Understanding Their Differences and Use Cases Weiterlesen »