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Article URL: https://pgext.cloud

Comments URL: https://news.ycombinator.com/item?id=49045376

Points: 1

# Comments: 0

Article URL: http://hintjens.com/blog:81

Comments URL: https://news.ycombinator.com/item?id=49045372

Points: 1

# Comments: 0

https://blazerules.dev

I initially wanted to make a sub-millisecond log parser in C++ but that blew into a embeddable decision engine, that can run YAML defined rules on incoming data. The rules are executed in a vectorized format on incoming data by reprojecting into a columnar format first, if it's not already. Depending on the payload size and rules complexity, the performance goes from 200K records/s to more than million records/sec, in terms of througput this would be around 200 MiB/s to 3 GiB/s on average.

Rules can be sql expressions too, or onnx models (numeric), window ops and quite a few more operations are supported.

It's comparable to DuckDB but for streaming data and on the fly decisions

Comments URL: https://news.ycombinator.com/item?id=49045369

Points: 1

# Comments: 0

Article URL: https://devpost.com/software/turboprefill

Comments URL: https://news.ycombinator.com/item?id=49045343

Points: 1

# Comments: 1

Article URL: https://www.cbc.ca/news/canada/nova-scotia/how-a-single-underscore-led-to-an-innocent-halifax-man-s-conviction-9.7283149

Comments URL: https://news.ycombinator.com/item?id=49045335

Points: 4

# Comments: 2

Article URL: https://rocm.blogs.amd.com/artificial-intelligence/instella-moe/README.html

Comments URL: https://news.ycombinator.com/item?id=49045333

Points: 2

# Comments: 0

Article URL: https://earendil.com/posts/prompt-caching/

Comments URL: https://news.ycombinator.com/item?id=49045285

Points: 1

# Comments: 0

Article URL: https://hintjens.wdfiles.com/local--files/books/psychopathcode.pdf

Comments URL: https://news.ycombinator.com/item?id=49045279

Points: 3

# Comments: 0

I’m not a software engineer, but I’ve been running an experiment to see whether agentic development could produce a useful prototype on top of an existing SaaS codebase.

The codebase is 1M+ lines, 15 years old, hosted on Azure, and primarily written in C# and React.

The prototype needs to be available for customer testing in September. No developers were available to work on it full-time, although I could occasionally get help with specific technical issues. An engineer will evaluate the implementation in August and decide how much confidence we can have in the AI-generated code so that we can decide how to “convert” the code to production-grade.

My question is what I can do before August, primarily using AI tools and manual testing, to make the code as robust and reviewable as possible. I want to increase the likelihood that the AI-generated code would be so good that the path to production-grade would be closer to “copy-paste” than building everything again from scratch.

Environment The prototype is being developed in a separate branch, deployed to a separate internal environment, and connected to its own database and schema.

Planning process We interviewed customers in June and turned the resuls into an MVP spec.

The process was approximately: 1. Write a PRD. 2. Use an LLM to convert the PRD into an architecture document, which was reviewed by an architect. 3. Create product designs consisting of screen images and md files containing interaction details ans edge cases with Claude Design. 4. Use an agent to break the work into epics using the PRD and architecture document as guardrails.

The epics were the most granular planning artifacts that received review by me and the architect.

Development process The development flow was intended to run with little intervention: 1. A planner agent converted epics into md story files and Jira stories 2. A coding agent implemented the stories including tests and opened PRs 3. A reviewer agent reviewed the PRs, requested changes, and merged them into the prototype branch

The coding agent polled PRs for review comments and could escalate issues back to the planner.

Agents also had a “stop and ask” list for decisions they were not allowed to make autonomously. I (and a few times an engineer) were involved by resolving those escalations and by manually testing the accumulated changes end to end each day.

Most implementation and initial review were done with Claude-based agents. For riskier PRs, I also used Codex as a reviewer. The second-model review found substantially more relevant issues in the Claude-generated code, but token quotas limited the use.

I had separate refactoring and harden runs for the code as well.

Results The planning and setting up the environment and agentic flow took about two weeks and then the agents built the whole MVP in about two weeks. Size-wise it was 13k lines of functional code + the same amount for tests.

What I would like advice on Assuming that I cannot get substantial developer involvement before the review, how can I increase the likelihood that the code is as close to production-grade as possible?

Here are some of the questions I have been thinking about: 1. What checks or development loops would give the largest improvement in confidence before an engineer reviews the code? 2. How would you use independent agents or models to reduce the risk that the coder and reviewer make the same incorrect assumptions? 3. Should tests be generated by a separate agent from the one that wrote the implementation? 4. What documentation or evidence would make the eventual engineering review faster and more reliable? 5. If you had only a few weeks to improve this prototype before handing it to an engineer, what would you prioritize?

Comments URL: https://news.ycombinator.com/item?id=49045271

Points: 1

# Comments: 0

Article URL: https://blog.owulveryck.info/2026/06/25/from-isolated-agents-to-agentic-mesh-orchestrating-sdlc-with-a2a-and-ap2.html

Comments URL: https://news.ycombinator.com/item?id=49045263

Points: 1

# Comments: 0

Article URL: https://post.substack.com/p/against-claudefishing

Comments URL: https://news.ycombinator.com/item?id=49045198

Points: 2

# Comments: 0

Article URL: https://zenodo.org/records/21515348

Comments URL: https://news.ycombinator.com/item?id=49045193

Points: 1

# Comments: 0

Article URL: https://www.technologyreview.com/2026/07/24/1140790/the-quest-to-keep-organs-alive-outside-the-body/

Comments URL: https://news.ycombinator.com/item?id=49045182

Points: 1

# Comments: 0

Article URL: https://brett.coulstock.id.au/markup-language-zoo.html

Comments URL: https://news.ycombinator.com/item?id=49045162

Points: 1

# Comments: 0

Article URL: https://kitsumed.github.io/blog/posts/android-may-soon-restrict-on-device-adb/

Comments URL: https://news.ycombinator.com/item?id=49045159

Points: 1

# Comments: 0

Article URL: https://awfulwoman.com/notes/2026/06/01/1838/

Comments URL: https://news.ycombinator.com/item?id=49045141

Points: 1

# Comments: 0

I feel like recent models have been consistently getting more sloppy and increasingly claude-ism heavy (load-bearing seams galore) with every new release.

I was hoping this trend would reverse in newer major version releases but I just tried out Opus 5 and it's similarly shit at writing. Fable is slightly better but not by far (and of course ridiculously more expensive). Adding skills helps but only mildly and not much at all for longer prose.

Have you guys used any model lately that you liked for its writing style? I remember being pleasantly surprised by GPT-4.5 but since that's been discontinued, I am on the lookout for a better candidate.

Comments URL: https://news.ycombinator.com/item?id=49045140

Points: 1

# Comments: 0

I am re-implementing postgresql from scratch in rust, with little twists..

Comments URL: https://news.ycombinator.com/item?id=49045134

Points: 1

# Comments: 0

Article URL: https://clickhouse.com/blog/postgres-fdw-pushdown-negotiation

Comments URL: https://news.ycombinator.com/item?id=49045063

Points: 1

# Comments: 0

Article URL: https://arcprize.org/leaderboard

Comments URL: https://news.ycombinator.com/item?id=49045040

Points: 7

# Comments: 0