Could Anthropic's Claude CoWork Say "Checkmate" to Palantir's Artificial Intelligence Platform (AIP)? - AOL.com
Could Anthropic's Claude CoWork Say "Checkmate" to Palantir's Artificial Intelligence Platform (AIP)? AOL.com
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How I Track My AI Spending as a Solo Dev (Without Going Broke)
I ship solo. No team, no finance department, no one reviewing expenses but me. When I started using LLMs heavily in my workflow — Claude for code review, GPT for drafts, a bit of Gemini here and there — I told myself I'd keep a close eye on costs. I had a vague sense of what I was spending. Turns out "a vague sense" doesn't cut it when you're getting invoiced. So I built a system. Or rather, I cobbled one together after getting burned. The Moment That Changed How I Think About This I was three weeks into a heavy coding sprint. I had Claude open basically all day — asking it to review diffs, explain errors, help me write tests. Normal stuff. Then my monthly statement hit. Not catastrophic, but more than I'd mentally budgeted. The frustrating part wasn't the money. It was that I had zero vis
I Built an MCP Server That Understands Your MSBuild Project Graph — Before You Build
Ask your AI coding assistant about your .NET solution structure and watch it hallucinate. It'll guess at project references, miss TFM mismatches, and confidently tell you things that aren't true — because it has no way to actually evaluate your MSBuild project files. Existing tools like BinlogInsights require you to build first, then analyze the binary log. That's useful, but it means you need a successful build before you can ask questions. What if your solution is broken? What if you just want to understand the dependency graph before a migration? I built MSBuild Graph MCP Server to fill this gap. It evaluates MSBuild project files directly — no build required — and exposes the results through 10 MCP tools that any AI assistant can call. What It Does Install it as a .NET global tool: dot
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How I Track My AI Spending as a Solo Dev (Without Going Broke)
I ship solo. No team, no finance department, no one reviewing expenses but me. When I started using LLMs heavily in my workflow — Claude for code review, GPT for drafts, a bit of Gemini here and there — I told myself I'd keep a close eye on costs. I had a vague sense of what I was spending. Turns out "a vague sense" doesn't cut it when you're getting invoiced. So I built a system. Or rather, I cobbled one together after getting burned. The Moment That Changed How I Think About This I was three weeks into a heavy coding sprint. I had Claude open basically all day — asking it to review diffs, explain errors, help me write tests. Normal stuff. Then my monthly statement hit. Not catastrophic, but more than I'd mentally budgeted. The frustrating part wasn't the money. It was that I had zero vis
Agent Middleware in Microsoft Agent Framework 1.0
A familiar pipeline pattern applied to AI agents Covers all three middleware types, registration scopes, termination , result override, and when to use each Not a New Idea If you have used ASP.NET Core or Express.js , you already understand the core concept. Both frameworks let you register a chain of functions around every request. Each function receives a context and a next() delegate . Calling next() continues the chain. Not calling it short circuits it. That is the pipeline pattern a clean way to apply cross cutting concerns like logging, authentication, and error handling without touching any business logic. Microsoft’s Agent Framework applies this exact pattern to AI agents. The next() delegate becomes call_next(), the context object holds the agent’s conversation instead of an HTTP
Meta freezes AI data work after breach puts training secrets at risk
In short: Meta has suspended its collaboration with Mercor, a $10 billion AI data startup, after a supply chain attack exposed what may be the AI industry’s most closely guarded secrets: not just personal data, but the training methodologies that power the world’s leading large language models. The breach, carried out via a poisoned version of [ ] This story continues at The Next Web
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