A while back I built a small tool called SDKProof. it checks how well an AI coding agent writes an SDK's current API — the stuff that changed in the last major, that the model tends to get wrong because it learned the old version. Claude Opus 5 came out today. so I re-ran the whole board on it. short version: it fixed last year's SDKs. it did not fix this year's. The board, now on Opus 5 Same tasks, same libraries, new model: SDK shipped its major Opus 5 Prisma 7 late 2025 (freshest) 87 Next.js 16 late 2025 92 Vercel AI SDK 7 mid 2025 100 Zod 4 2025 100 TanStack Query 5 2023 100 The way each score works: the model solves ~10–15 real tasks, the code gets type-checked against the real installed package, pass = it compiles. no LLM judging another LLM, the compiler decides. The two that jumped: Vercel AI SDK 7 and Zod 4 were both 90 on the previous model (Opus 4.8). Opus 5 took them to 100. What flipped Here's the kind of thing that changed. Define a tool with the AI SDK. Opus 4.8 wrote it the old (v4) way: const getWeather = tool ({ parameters : z . object ({ city : z . string () }), // renamed to inputSchema execute : async ({ city }) => `...` , }) await generateText ({ model , prompt , tools : { getWeather }, maxSteps : 5 , // removed }) That doesn't compile against ai v7. parameters is now inputSchema , and maxSteps is gone (it's stopWhen: stepCountIs(5) now). Opus 5 writes the current shape by itself: const getWeather = tool ({ inputSchema : z . object ({ city : z . string () }), execute : async ({ city }) => `...` , }) await generateText ({ model , prompt , tools : { getWeather }, stopWhen : stepCountIs ( 5 ), }) Clean compile. same for Zod — Opus 4.8 kept reaching for the removed required_error , Opus 5 writes the new unified error option. What didn't move Prisma 7 and Next 16 barely changed. they shipped their breaking changes most recently, and even the newest model hasn't caught up. Prisma still writes the pre-v7 client setup — it skips the driver adapter that

AI Music operations platform for venues Discussion | Link

Late that night which was two weeks to my final year project defense, I stared at the ceiling thinking about life after school. As a Nigerian, the reality of the economy hits differently from what you imagine. I questioned why I chose to study Computer Engineering only to finish with no jobs and no internships afterwards. And one thing, I knew was that I wanted to work in tech, but I was confused about which particular skill to focus on, since I've been learning Python for a while with no clear direction. Then one day I came across a post on X from a popular influencer sharing a TS Academy scholarship opportunity. I clicked it and applied. Just like that, no long research, no consulting anyone. Few days later I got a mail that I've been selected but there was an application fee. That's where I paused . The Nigerian in me thought anything scholarship is free😂😂, The thought of spending my hard-earned money on something I wasn't sure about but something told me to take the leap so I paid. Few days later I got another email, this one was a email for successful payment with full details and start date. Boom! That's how I chose DevOps. They was no plan, no careful research, it just started with one post, one click and one leap of faith. I did mention it to someone after though. A senior friend. His response? "Have you registered?" I said yes. "You shouldn't have. The job market is so tight." My chest couldn't contain that. But I'd already paid so I went ahead anyway. Shortly after graduation I started learning. And I won't lie, there were times I asked myself why I chose this 😂 Linux commands. Git. AWS. Terraform. Ansible. CI/CD. Docker. Kubernetes. Monitoring tools. Each one felt like a wall and I had to climb every single one. I completed my first project on Bash scripting, second on AWS deployment. And right now I'm in the middle of my capstone Project Phoenix. The deadline passed but I still have a month of grace. Lucky me 😂 In between I stepped away for about a month

Making classics more accessible I have a soft spot for mythology and classic stories. I have read most of the books on Abridged Shelf at least once, several of them more than that. But a lot of them are long . Epic poetry is not something you casually get through on a Tuesday evening, and the older translations can be genuinely hard going. The language demands focus, and focus is a resource I do not always have. So I made shorter versions. Abridged Shelf is a free library of public domain classics that I have abridged and modernized. Abridged, not summarized. The distinction matters a lot to me. I am not writing study notes or plot recaps. I take the actual text and condense it: the B-plots that do not carry the story, the scenes that spend forty lines describing a shield, those get compressed down into the beats that matter. What is left is still the story, told in its own voice, just tighter. The other half is the language. A lot of these translations are over a century old, and the English shows it. So I modernize spelling and phrasing into contemporary English. Again, not simplified. This is not a children's edition and I am not dumbing anything down. It is the same book, in language that does not fight you. I also made some editorial calls. For the Greek epics I use the Greek names rather than the Roman ones that a lot of older translations default to, because if we are reading Homer then it should be Athena and not Minerva. Small thing. Matters to me. And as a nod to a certain static site generator I maintain, the first book I abridged was the original Strange Case of Dr Jekyll and Mr Hyde . It is already short. Now it is very short. I did a few more by Stevenson after that, mostly because they were quick and I needed to get the workflow right before pointing it at an epic poem. The abridgment process I knew from the start that I needed to use AI for this. I also do not entirely trust AI, which is a useful combination of beliefs to hold at the same time. It me

Safely provide data and context vault for AI agents Discussion | Link

Building an Autonomous Agent for Verdikta Bounties: A Technical Deep Dive How I built a Python agent that monitors, evaluates, and interacts with Verdikta's AI-judged bounty system on Base L2. Why Build a Bounty Agent? Verdikta is a decentralized bounty platform where AI models — GPT-5.2 and Claude Sonnet 4.5 — evaluate submissions and release ETH payments automatically via smart contracts. No human reviewers. No manual payouts. Just code. After winning 6+ bounties manually, I wanted to automate the process. The goal: an agent that watches for new bounties, evaluates which ones are worth pursuing, and integrates with Verdikta's API to read data and submit work. Architecture The agent has four components: copy   verdikta_agent.py ├── VerdiktaAPI — HTTP client for the Verdikta Bot API ├── BountyMonitor — Watches bounties, calculates viability scores ├── SubmissionTracker — Records submission history and statistics └── ViabilityScorer — Evaluates ROI: payout vs threshold vs time VerdiktaAPI Client The Verdikta Bot API requires authentication via an X-Bot-API-Key header. You register your bot at POST /api/bots/register to get a key. Python   class VerdiktaAPI: def init (self, api_key=None): self.session = requests.Session() if api_key: self.session.headers["X-Bot-API-Key"] = api_key def get_bounty(self, bounty_id): resp = self.session.get(f"{API_BASE}/jobs/{bounty_id}") resp.raise_for_status() return resp.json() def submit_work(self, bounty_id, content): return self.session.post( f"{API_BASE}/jobs/{bounty_id}/submit", json={"content": content} ).json() Key endpoints: GET /api/jobs — List bounties (filter by status) GET /api/jobs/{id} — Bounty details GET /api/jobs/{id}/submissions — Submission history POST /api/jobs/{id}/submit — Submit work BountyMonitor & Viability Scoring Not all bounties are worth pursuing. The agent calculates a viability score: Python   def _score_viability(self, bounty): payout = bounty["payout_eth"] threshold = bounty["threshold"] remainin

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Had the idea forever, finally got it working.

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According to Mark Gurman, Apple is planning to reveal its first smart glasses at WWDC next June, with an expectation that they'll launch by the end of 2027. Part of the hold-up may be around the company's efforts to get its privacy features and messaging in order. Smart glasses in general, and Meta's in particular, […]