[music] So companies spend millions on AI tools for software engineering. But do we actually know how well these tools work in the enterprise or are these tools just all hype. To answer this and for the past two years, we've been researching the impact of AI on software engineering productivity.And our research is time series because we look at get historical data, meaning we can go back in time. And it's also cross-sectional because we cut across companies. And the way we use to measure most of the of the ...

RT Avi Chawla (@_avichawla)An MCP server that detects production-grade code quality issues in real-time!Even though AI is now generating code at light speed, the engineering bottleneck has just moved from writing to reviewing, and now devs spend 90% of their debugging time on AI-generated code.AI reviewers aren't that reliable either because they share the same fundamental blind spots as AI generators do:- They pattern match, not proof check.- They validate syntax, not system behavior.- They review code, no ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/xGHVOyNLz1Avi Chawla (@_avichawla):An MCP server that detects production-grade code quality issues in real-time!Even though AI is now generating code at light speed, the engineering bottleneck has just moved from writing to reviewing, and now devs spend 90% of their debugging time on AI-generated code.AI https://t.co/MoAUkPZQiF ...

An MCP server that detects production-grade code quality issues in real-time!Even though AI is now generating code at light speed, the engineering bottleneck has just moved from writing to reviewing, and now devs spend 90% of their debugging time on AI-generated code.AI reviewers aren't that reliable either because they share the same fundamental blind spots as AI generators do:- They pattern match, not proof check.- They validate syntax, not system behavior.- They review code, not consequences.I have been ...

RT Jimmy Song (송재준) (@jimmysong)Within the datacarriersize/OP_RETURN change is hidden a change to how many OP_RETURN outputs are allowed. Previously, it was 1. With the datacarriersize change, it's now as many as the user wants.The technical justification for multiple OP_RETURNs now being standard is completely unconvincing. To quote @theinstagibbs:"The motivation for doing this is for situations where you cannot commit to all data efficiently otherwise. Think SIGHASH_SINGLE | ACP scenarios. The datacarrier ...