标签：#ha

Hi HN, I am co-founder of Kickstage, a software company specializing in solutions for the electrical industry and lately grid operators. We are hiring engineers from different backgrounds, a lot of them software developers with limited experience in the sectors. Deep domain knowledge is key in our industry however, so we are constantly teaching the basics of power flow analysis, active vs reactive power, transmission line properties etc. With Jupyter notebooks and the Python console only, that's

As long as we don't have AGI or superintelligence, we still need good software engineers. And even if we do reach AGI and coding is "solved," are we really supposed to just trust the generated code? Outsourcing our cognitive thinking and understanding isn't the way to advance as humans, nor is it the way to stay ahead of AI. If we give that up, how do we limit AI slop? And how would we even pull the plug, if it came to that? Understanding computer science concepts and developing good software en

You may have heard that OpenAI released its first piece of hardware this week. You may not have heard about the ChatGPT basketball.

Exposing your data over MCP is the easy part. Designing a tool an agent uses well is the hard part. An agent can only use a tool it can read, so the work is shaping the tool for how the model calls it, not just for the human looking at the result. These are the techniques behind @bonnard/mcp-charts and the visualize tool. Discovery-first, so the agent stops guessing An agent that guesses your schema writes wrong queries. So the first tool the agent meets is a discovery tool. It calls visualize_read_me to load the chart options, the tool schema, and worked examples before it ever calls visualize , and an explore_schema tool to learn your tables and columns before it writes SQL. The agent reads, then acts. A small set of purpose-built tools The temptation is one tool per metric, or a single tool that takes arbitrary SQL and hopes. Both fail: too many tools blow the agent's attention budget; one firehose tool gives it no guardrails. Bonnard ships a small set, discover, query, visualize, each with a narrow, obvious job. The agent picks the right one because there are few of them and each does one thing. // a small, purpose-built set, not one tool per metric server . registerTool ( " explore_schema " , { /* list tables + columns */ }, listSchema ); addCharts ( server , { runSql }); // registers visualize_read_me + visualize Compact, honest responses A tool that returns 10,000 raw rows poisons the context window and the agent's next decision. Bonnard's responses are sized for a model to read: Row caps with a completeness flag. Results are capped and tagged partial or complete , so the agent knows whether it is looking at everything. Partial-result warnings. When results are capped, the response says so and tells the agent not to sum or average the visible rows, use a measure instead. Summaries over dumps. The chart comes back with a compact text summary the model can reason over, not just an image it cannot read. Errors that guide the next action A bare "error: invalid co

https://www.kimi.com/en Kimi K3 Intelligence, Performance & Price Analysis: https://artificialanalysis.ai/models/kimi-k3

I've found the most engaging way to practice techniques for AI-assisted development and test models is to build fun side projects in vanilla JS. I spent many hours playing (and studying and editing) QBasic Gorillas, and this is a vanilla JS implementation using Fable and Opus. Play 1-on-1 hotseat or against the computer. A bit of extra camera snazz as well.

I created the kind of product that would actually help me pass the coding round. Never in my life have I ever advanced past the coding round. Couldn't do it to save my life. But now, I have a tutor who can explain and morph problems and explain them to me in a smooth brained way until I get it. Since the visuals also follow the tutors explanation, I can iterate until a DSA pattern sticks and solution patterns are easy for my brain to reproduce when in the hot seat. Try it out, find a problem tha