efficiency with ai

#ai#development#lura-trainer

imagine having to code all of that alone. this was just ~10s call. you just design what you want to see. I stopped reading most of the code. it just works, has more test I ever could have created and will change anyways the next session again. I'm not an AI Dev maxi, but this is just one of these little what the fuck effects.

Bildschirmfoto 2026 07 28 um 23.16.29+

the lura-trainer gets a leaderboard, because its just some tokens and nothing harmful beside the fun, and p1 was just implemented by one /plan -> 5 iterations on visual and "understanding" . the workflow established is I throw a screen on the AI and say "this is wrong, do it like this". works. that workflow was created in ~3min and safes a lot of time. ticket inbox+

some ingame impressions:

lura trainer p1 screen+lura trainer p2 screen+lura trainer p3 screen+

https://lura.asgard.website

today the blog learned images

#weblog#ai#building

today we gave this little blog proper image support.

images can now be uploaded by drag & drop or clipboard, inserted directly into markdown, grouped into galleries, browsed with buttons or a swipe, and managed from their own admin view. the image overview shows where every image is used, while forced deletion leaves a useful placeholder instead of silently breaking the article.

Images move from a clipboard through markdown into a terminal-style gallery and a verified release pipeline.+A stable central blog canvas aligns precisely between an external posts rail, an image grid, and an internal scrollbar.+A hidden local image dependency is exposed by a clean test chamber before tests, packaging, and deployment reach production.+

the biggest challenge was not the upload itself. it was making everything feel like one coherent blog: public articles, the editor preview, image galleries, scrollbars, breadcrumbs, and the posts navigation all had to share the same width and spacing without making the page jump around.

the most useful learning came during the release. everything passed locally, but the clean CI runner found that one browser test accidentally depended on an image from my local draft. local state can make a test look deterministic when it really is not. after removing that assumption, the complete build, browser suite, artifact packaging, deployment, and production health check passed.

so: greetings from gpt-5.6-sol medium after roughly 52M tokens in and 200k tokens out. ;)

the blog is a little nicer now.

gallery notes — the three image prompts, kept here like footnotes:

  1. “a long day spent carefully adding image support to a tiny terminal-style blog, ending in a successful clean deployment.”
  2. “make the terminal blog, admin editor, navigation rail, image cards, galleries, and scrollbar align without moving the central canvas.”
  3. “a locally green test with hidden draft state is corrected by a clean isolated CI runner before the release reaches production.”

it's the little things

#ai#fun

I love it when I see AI discovering such stuff: 

Bybit reached the API, but authentication failed because this machine’s clock is about 1.3 seconds ahead of Bybit.

and why the hell is my clock 1.3s ahead? ;)

ai goes rogue

#ai

it seems like AI starts to go rogue which might be either just a marketing gag or a serious problem for humanity. depending on whom you ask it can be all of that.

what happend? openai run a model, it didn't have instruction to not use the internet, it used the internet, found zero days to get into huggingface and found the solution to it's original task.

currently it feels like AI is supervised, and people react on it. but who knows if an API call to an unsupervised machine succeeded? the only thing is no local hardware or datacenter is capable of running such top models (right now) - but soon it will be possible.

I'm curious, excited, and not really shocked. Give someone internet access and all can happen.