The tutorial-to-production gap Every RAG tutorial follows the same arc. Load some...
Given the expansion of the fed/state/local/corporate surveillance net, do you find yourself expressing yourself less freely on the internet than you did five years ago?
Hi everyone, We've open-sourced Tri-Net v2, the official implementation accompanying our recently published Scientific Reports (Nature Portfolio) paper: "Tri-Net: Unified Deep Learning for Skin Lesion and Symptom-Based Monkeypox Detection" Rather than releasing only training scripts, we rebuilt the project as a reproducible research framework. Highlights: • Leakage-free data preparation pipeline • Multiple CNN backbones (ConvNeXt-Tiny, DenseNet201, Inception-ResNetV2) • Ensemble and feature-fusion strategies • Grad-CAM explainability • Cross-validation and statistical evaluation • Docker support • GitHub Actions CI • PyPI package (`pip install mpox-trinet`) • CLI for training, inference, and benchmarking The paper has already received over 1,100 article accesses in its first week, and we hope making the implementation fully open-source will help others reproduce, validate, and extend the work. GitHub: https://github.com/Sudharsanselvaraj/Synergistic-Deep-Learning-for-Monkeypox-Diagnosis PyPI: https://pypi.org/project/Mpox-Trinet/ Paper: https://www.nature.com/articles/s41598-026-61490-x I'd really appreciate feedback on the implementation, reproducibility, code quality, or ideas for future improvements. Contributions and issues are very welcome! submitted by /u/Rich-Fruit-326 [link] [留言]
Open-source AI agent video editor with a real timeline Discussion | Link
This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry. I originally...
If you have worked with APIs for a while, you have probably realized that designing an API is only...
submitted by /u/pgEdge_Postgres [link] [留言]
My job-search tool had five functions whose only purpose was fixing JSON that Claude had just written. _clean_json_text , _fix_unescaped_newlines , _fix_single_quotes , _strip_markdown_wrapper , _extract_and_parse_json . There was also a sixth, _retry_json_fix , which took the broken JSON and sent it back to the model with a polite request to fix its own mess. I wrote every one of them, one bug at a time, over weeks. I was a little proud of them. That was the problem. How you end up with five parsers JobSearch is my personal tool, in production, single user: me. It ingests job offers from nine boards, and when I press "Analyze", Claude reads the offer against my CV and returns a structured verdict: score, recommendation, career track, the English level the ad actually requires. That verdict has to be JSON, because everything downstream is a database row, not prose. The first version did what every tutorial does. Ask the model for JSON in the prompt, take response.content[0].text , run json.loads on it. It worked in the demo and then production started teaching me things. The model wrapped the JSON in markdown fences, so I wrote a function to strip them. Sometimes it used single quotes, so I wrote a function to fix those. Then a description with a line break inside a string, so I wrote _fix_unescaped_newlines . Then a NaN where a number should be. Every fix was five lines, obviously correct, and came with its own tests. I still have the test names in the git history and they read like a confession: test_removes_trailing_commas , test_replaces_nan_with_null , test_replaces_infinity , test_unclosed_fence_still_strips_opening . By April the parsing layer was around 250 lines with seven strategies, chained, each catching what the previous one let through. The last resort was the AI self-repair call: if nothing parsed, send the broken output back and ask the model to repair it. A second API call, with real latency and real cost, to fix a formatting problem the first call
That 130-file PR that shipped KeyEcho 1.0 contained a decision I never wrote about: the desktop app moved from Tauri 1 + Vue to Tauri 2 + SolidJS. The same summer, upweb.dev moved from Nuxt 4 to SolidStart, and keyecho.app (SSR, five languages, Stripe checkout) was built on SolidStart from scratch. Three separate decisions, same answer every time. Scope first. This post is about defaults for my personal projects. Near the end I'll cover two places where I still use React, one of which runs very well. The criteria changed Most of the code in my repos is AI-generated now. My job has shifted from writing code to reviewing it. The old framework checklist put most of its weight on learning curve and developer experience. Both are worth zero now: the model writes five frameworks fluently and knows the rules of hooks better than most humans do. AI crushed the price of writing code. Two costs didn't move: the runtime bytes every user downloads, and the human hours spent reviewing. Pick your stack by the new prices. Runtime cost lives in the architecture. No prompt removes it, so it decides the framework. Review bandwidth is finite, and AI throughput will grind down any consistency that is maintained by verbal agreement, so it decides the styling layer. I have seen more than one React codebase a few years into its life: two animation libraries, two carousel components, three styling systems, each introduced for a perfectly good reason on some ticket, and the sum is a mess nobody can clean up. AI did not invent that drift. It made it an order of magnitude faster. React is no longer my default: size and performance Let me discard the weak arguments first. Dependency arrays, stale closures, re-render storms: I am not going to relitigate any of it. The model knows the rules of hooks cold, eslint-plugin-react-hooks catches most slips, and React Compiler now handles memoization. The ergonomics debate no longer produces a winner. What still produces a winner is the client. react-do