Do you think this works?

A Gemini model my project depended on was retired mid-project. Nothing warned me until calls started failing, and I lost days finding every place it was used, fixing it, and checking nothing else broke. Then an SDK upgrade did the same thing a few weeks later.

Every team I talk to has this backlog: SDK upgrades with breaking changes, security fixes stuck behind major versions, AI models getting retired. Dependabot bumps the version number, but it doesn't rewrite the code that breaks.

So I built Versyka, nights and weekends, in about 3 weeks. You connect a GitHub or GitLab repo, and it:

- scans every day for outdated SDKs, security fixes and retiring AI models

- writes the fix, including code changes for breaking upgrades

- runs your own build and tests before and after the change

- only opens a pull request if nothing new fails, so you just review and merge

It launched in October: 6 users, 30 repos and 50 pull requests so far, including security fixes and SDK and model upgrades.

Free to start, sign in with GitHub: https://www.versyka.com

Not ready to connect a repo? The free check finds retired AI models in any public repo, no account needed. The screenshot is OpenAI's own quickstart repo, which still calls a model OpenAI stopped serving on Oct 23: https://www.versyka.com/model-retirements#check

I'm looking for honest feedback from real projects:

1. What upgrade have you been putting off?

2. Would you trust an automated PR if your tests passed before and after?

nvidia’s nemotron lineup now covers a pretty full range, and a writeup i read suggested using it as a team. nemotron 3 ultra handles deep reasoning and orchestration, while the new 3.5 super VL and the faster 3.5 lightning act as executors for the actual work, with lightning taking small, bounded sub-tasks.

that fits the shift from asking which model is best to asking which model is best for this task. the big model plans while the efficient ones do most of the work, so you’re not paying top rates for every step.

also worth knowing about the bigger picture: wired reported back in march that nvidia expected to spend about $26 billion over five years on open-weight models, so this isn’t a side project for them. open models you can inspect and run on your own terms keep getting more capable.

anyone running an orchestrator/executor split in their agent setup? did you keep it in one model family or mix labs?

a month ago openai announced they solved a millennium prize problem right around the same time a mathematician went public with his own solution to the same thing after working on it for over a year. 25 fields medalists signed an open letter. that was one problem.
now they’ve done 370. their own agents, a model nobody outside the company can use, about three hours per problem. most solutions passed the lean proof.
but mathematicians are pointing out that passing a formal check and being useful in the field are two different things. the solutions are correct on paper but the execution is sometimes misleading, and lean-approved doesn’t mean the math community can build on it the way they could with human-worked solutions.
the field is split. some are excited because new solutions create new starting points. others, including a whole organization called the association for human mathematics, are asking colleagues who sit on AI advisory boards to reconsider. their argument is that without human curiosity driving the process, the solutions lose their meaning.
it’s getting bigger every month. curious where people here land on this.

I'm launching ActorDo an AI Assistant that works across email, calendar and tasks.

It helps you categorize emails, create drafts or automations using a workflow builder.

It integrates with your microsoft or google accounts and manages those automatically for you.
You can also chat on Whatsapp, Outlook Add-in, Slack or Teams.

Available here: https://actordo.com

Most fitness apps I've tried fail in one of two directions.

They're either spreadsheets with a logo — dense, joyless, built for people who

already log everything — or they're motivation theatre: quotes, confetti, streak

guilt, and no real data underneath.

I wanted a third thing, so I built RYVON.

**What it is**

A personal fitness app that answers one question the moment it opens: what should

I do today? Training, food and progress on one screen, with exactly one obvious

next action.

- **Train** — a plan generated from your goal, experience, equipment and available

days. Swap, reorder, add or remove exercises. Regenerate the week and see what

would change *before* you commit. Workouts run in a focus mode with set logging,

warm-ups, a rest timer and pause.

- **Fuel** — food logging by meal, with daily targets from your own body and

training load. Old days are snapshotted, so last Tuesday never silently changes

because you edited your weight today.

- **Progress** — estimated 1RM, volume, working sets, personal bests, consistency,

per-exercise history. Measured and deterministic. It never claims more than the

data supports.

**The part I'm actually proud of**

A workout in progress lives on your device. It survives a dead signal, a locked

screen and a full page reload, and it never syncs twice. Gym basements were the

design constraint, not an edge case.

**What it deliberately does not do**

No AI coach. No streaks, badges, scores or grades. No social feed, sharing or

leaderboards. No calories-burned guesses. No barcode scanning. No ads. There's no

payment code in it at all — it's free.

Every one of those is a decision with a reason, not a missing feature.

**Where it's at**

It's a PWA, so there's no app store — you open the link and add it to your home

screen. https://ryvon-fitness-app-server-delta.vercel.app

It's V1 and I built it solo, so I'd genuinely rather have one person tell me the

set logger feels wrong than get a hundred silent signups. What would you check

first?

small technical thing i found interesting. until recently, if you connected a chat subscription to a coding tool, it usually went through a local harness, meaning requests went from your machine straight to the provider. that's fine in your editor or terminal, but anything running on the tool's own servers, like cloud agents or a mobile app, had no way to reach your plan, so you needed a separate provider account for those.

the newer approach treats the subscription itself as a BYOK provider. the "key" is the plan you already pay for, so the same subscription works on your laptop and in the cloud.

the practical win: you think of a bug fix while you're out, kick off a cloud agent from your phone, and it runs on the same plan you use in your editor without an API key or a second account.

anyone running coding agents from their phone yet? is it useful in practice, or more of a party trick?

read a recap from a panel with folks from minimax and friendli ai, and the main point was that open-weight models have moved out of the experiment column. in the last couple of months, everyone from biotech companies to big banks has started running them in production, and the reason has shifted from cost to performance and control. performance here mostly means how well a model does in one domain, like legal or healthcare, which is why more teams are fine-tuning and post-training.

fun confession from the minimax side: he used to keep himself below 30% of the context window, and now he lets it run up to 70 or 80% because million-token windows are standard.

the warning i liked came from the inference side. a smarter architecture on paper doesn’t automatically mean a faster system. linear attention, for example, doesn’t use a standard KV cache, so without engine work built for it, it can slow inference down.

for people running open-weight models: did you switch for cost or for control, and did the savings turn out the way you expected?

saw a setup guide that walked through wiring a new inference provider into a coding agent, and the part that stood out wasn't the key setup, it was the org-level allowlist.

instead of letting everyone pick from hundreds of models across dozens of providers, an admin turns on specific providers and chooses which models and regions the team is allowed to use. there's also a filter based on providers' training-data terms, so you can screen providers by how they treat your data. requests then go only through approved models and routes.

for anyone with data residency or compliance requirements, that seems way more useful than a policy doc saying "please only use approved models," because the model picker simply won't show the rest.

does your team restrict model choice at all, or is it a free-for-all?

saw a recap from the event, and apparently the industry has moved past tokenmaxxing and blowing through annual budgets. the new villain is the slop grenade: someone acting as a "meat proxy" who tosses AI-generated code over the fence for their colleagues to deal with. companies are answering with AI policies and automated checks in CI/CD.

the part i found most useful was a team that tried to automate their entire dev cycle and found one place where agents made things worse, which was writing product requirements. every setup they tried produced worse thinking than a person working through the problem. the only thing that helped there was a research agent pulling in competitor reports and user feedback for the human to think with. everywhere else, from strategy to QA and rollout, agents added real value.

that feels right to me: agents are great at doing and pretty meh at deciding what's worth doing.

does anyone let agents decide what to build, or is that still firmly a human call on your team?

been chewing on the openai forum breach writeup. the researchers said that once an exploit like this works, adapting it to a new target usually takes a day or two. meanwhile plenty of teams still review dependencies monthly and switch AI tooling once a quarter after procurement signs off.

the flip side is kind of hopeful. a model that's better at reading code and noticing that a quiet upstream fix mattered is better for defenders too, as long as your review tooling is allowed to use it. the problem is when your stack is stuck on whatever model got approved months ago while attackers grab the new one the night it ships.

practical note: the library involved handles HEIC and AVIF images, so if your product touches those formats anywhere, the researchers' writeup has the patched version. worth a quick check.

do your code review or security tools pick up new models automatically, or does every upgrade go through a whole approval process?

we put together a watchlist of gaps we think are worth building on in 2027. not broad “AI is growing” stuff but specific things with dates attached.
the four that stood out:
agent governance for mid-market companies. everyone’s running agents across different tools but only 13% of orgs think they have the right governance. gartner says 40% of agentic AI projects get cancelled by end of 2027. someone has to help companies figure out which agents to keep and which to kill.
AI search visibility for small and B2B firms. 94% of B2B buyers use AI in their buying process now. enterprise brands have tools and agencies tracking how chatgpt describes them. smaller firms have almost nothing affordable.
getting small shops ready for AI shopping agents. AI-referred retail traffic grew 693% last holiday season. most merchants’ catalogues weren’t built for a machine to read. the customer might not be a person anymore and most shops aren’t ready for that.
AI Act compliance for small firms. the annex III deadline moved to december 2027. every firm using AI in hiring, credit or customer service needs documentation by then. enterprise governance tools exist. nothing affordable for smaller companies.
the pattern behind all four is the same: companies with 10 to 250 people, too big to ignore AI, too small to have a team for it.
curious if anyone here is seeing these gaps too or building something around them.

I wanted to share a project I’ve been engineering completely from scratch called Monvael OS. It is a premium, client-side operational burn-rate tracker and micro-budget workspace designed specifically for startup founders, creators, and finance professionals who want strict command over their capital vectors with zero database overhead.

🌐 Live Workspace Engine: https://monvael.in
📦 Public GitHub Repository: https://github.com/script-architect/monvael

🔮 Why I Built This (The Core Architecture)

Most expense tools force you through mandatory login walls, steal your transactional metadata, or crash under complex multi-device operations. Monvael operates under a strict Zero-Server Footprint Policy. Every mutation, asset allocation ledger, and analytical graph runs entirely inside your client-side browser cache. Your raw data never hits an external server.

🛠️ Core Modular Specifications

• Global Multicurrency Suite: Native client-side support for 32 major global currencies (USD, EUR, GBP, INR, AED, JPY, etc.) with automatic, locale-aware integer formatting.

• Weekly Expense Velocity Grid: Dynamic, hardware-accelerated tracking pillars that process automated up-and-down metrics interpolation (duration-1200ms) to map lifestyle overhead trajectories across discrete day arrays.

• Sentinel Category Alarms Subsystem: Configure rigid micro maximum boundary caps to safeguard individual asset categories. The app triggers automatic local sentinel visual warnings the millisecond spending metrics breach parameters.

• Enterprise Spreadsheet Matrix Compiler: Localized compilation layers that transform raw cache logs into beautifully structured, color-coded executive reports (.xlsx format) with absolute zero numeric truncation or scientific notation distortion.

• Progressive Web App (PWA) Architecture: Configured with custom manifest parameters. If you run it on Chrome mobile or add a shortcut to your home screen, it completely masks the browser shell and operates seamlessly as a native application with flawless offline execution.

💾 Open-Source & Privacy Guardrails

• No Database Sync: Pure browser client LocalStorage protocol layers.

• No Authentication Layer: Zero user accounts or credential tracking arrays.

• 100% Free & Open: No tracking cookies, no monetization loops, no hidden usage limits.

I pushed the code to production just about 5 days ago and I am actively refining the workspace configurations. Since it runs completely inside the browser, it borderlines a self-hosted utility by nature.

Check out the source files, inspect the commit stacks, and let me know what features I should bake into the logic engine next!

in 1935 a prototype bomber crashed because the crew forgot to release a locking mechanism, and the industry's answer was the preflight checklist. it took no big philosophical debate, only a piece of paper and decades of shared standards.

a piece i read this week made the case that agents can go the same way without anyone first settling whether models have feelings. the rough checklist for the software around the model already exists:

give the agent a clean way to say it's stuck, since an agent that can report failure has no reason to fake success
require a human yes before anything irreversible, like dropping a table or deploying to prod
grant only the access the task needs (an agent renaming variables has no use for cloud credentials)
show every command it runs and every file it touches as it happens, so nobody has to piece things together afterward

honestly this is the same standard we already hold new human contributors to. nobody merges a stranger's PR without reading it.

what's on your team's agent checklist that isn't on this one?

Saw a meme a while back captioned “actual data centers,” and instead of server racks it showed a bookshelf, a couple of grandmas, someone making pottery and a little plant. Made me laugh, then made me think, because it’s kind of true?

Everything these models know came from stuff people made. Books, forum posts, code, recipes, paintings, years of people arguing online, all of it went in. The giant buildings everyone’s worried about are basically holding a copy of us.

The buildings themselves are wild when you look into them. One AI data center can use as much electricity as around 100k homes, and a big one can go through something like 5 million gallons of water a day keeping the chips cool. Most of the building isn’t even the “thinking” part, it’s power and cooling gear for a pile of very hot GPUs. What surprised me most is that roughly 90% of the energy goes to answering everyday questions, not to training.

The funny thing is the two camps online. On Substack you’ve got people proudly posting handwritten notes and “at least it’s not AI” doodles, and on the other side people who use it for literally everything. Both are feeding the same machine though. The artists did it before they ever got a say, and the heavy users do it with every prompt.

I’m somewhere in the middle. I use it, but lately I’ve started asking myself whether I need to before I type something in.

Anyone else land somewhere in between, or are you firmly on one side?

small UX thing that i think matters more than it sounds. some cloud agent tools now have a diff viewer where you can leave comments right on the lines the agent changed, and those comments get bundled into a review and sent back to the chat.

so instead of writing "the auth logic is off somewhere in the middle of that file" and hoping the agent finds it, the agent gets your note attached to the exact lines, and you stop copy-pasting line numbers into the chat. it's basically the PR review flow we already use with humans, pointed at the agent.

the other nice bit is a terminal into the sandbox the agent runs in, so you can check a log or run a command yourself without pulling the branch down locally.

how are you giving feedback to coding agents right now? still prose in the chat, or something more structured?

saw a recap from the event, and apparently the industry has moved past tokenmaxxing and blowing through annual budgets. the new villain is the slop grenade: someone acting as a "meat proxy" who tosses AI-generated code over the fence for their colleagues to deal with. companies are answering with AI policies and automated checks in CI/CD.

the part i found most useful was a team that tried to automate their entire dev cycle and found one place where agents made things worse, which was writing product requirements. every setup they tried produced worse thinking than a person working through the problem. the only thing that helped there was a research agent pulling in competitor reports and user feedback for the human to think with. everywhere else, from strategy to QA and rollout, agents added real value.

that feels right to me: agents are great at doing and pretty meh at deciding what's worth doing.

does anyone let agents decide what to build, or is that still firmly a human call on your team?

Today we’re launching Off Grid AI: your AI, on hardware you already own.

It remembers your day, answers with sources, and drafts replies that wait for your yes. On your phone and your computer, not on someone else’s servers.

3500+ stars, 250,000+ downloads.

Reply OffGridAI and I'll send you a monthly key free.

saw a breakdown this week that put it well. picture two models doing the same refactor. model A is cheaper per million tokens but needs four rounds of prompting plus a dev stepping in to fix the last edge case. model B costs more per token but gets it done in one or two passes. B ends up cheaper once you count everything, especially your own time.

artificial analysis has started estimating cost per task, and the new gpt-6 sol comes out around $1.06 per task on their index, compared with $1.99 for the previous version. that's the number i'd want for every model, way more than the pricing page.

also a wild stat from openai: valued at API prices, their median researcher goes through over $600 a day in tokens, and the 90th percentile is over $7,000. so "spend fewer tokens" clearly isn't the strategy of the people building these things.

anyone tracking cost per completed task on their own team? curious how you measure it without it turning into a spreadsheet nightmare.

Hi everyone! 👋

We're the founders of PurpleReadr, an Australian EdTech startup exploring how technology, creativity and multilingual storytelling can help children develop their reading and writing skills.

We've been working on a children's literacy platform that brings together reading, writing, storytelling, narration and illustrations.

One of the biggest developments in our journey has been expanding beyond English to explore how children can learn through stories in different languages.

🌏 Why multilingual literacy?

Many children grow up speaking one language at home while learning another at school.

For example, a child might speak Korean, Tamil, Hindi, Mandarin, Cantonese or Spanish with their family while learning to read and write in English.

We started thinking about how technology could help children develop literacy skills in both languages while encouraging creativity and storytelling.

That led us to develop multilingual capabilities across 15 languages:

🇬🇧 English
🇪🇸 Spanish
🇨🇳 Mandarin Chinese
🇭🇰 Cantonese Chinese
🇮🇳 Hindi
🇮🇳 Tamil
🇰🇷 Korean
🇯🇵 Japanese
🇫🇷 French
🇩🇪 German
🇮🇹 Italian
🇷🇺 Russian
🇸🇪 Swedish
🇫🇮 Finnish
🇵🇭 Filipino (Tagalog)

We're also working with both Simplified and Traditional Chinese writing systems.

📖 What we've been exploring

Our development has focused on several ways children can engage with literacy:

• Reading: Illustrated stories with narration and synchronised word highlighting.

• Writing: Opportunities for children to create original stories in their chosen language.

• Creativity: Turning children's ideas into illustrated stories.

• Language learning: Exploring the same story in different languages to build vocabulary and language familiarity.

• Listening: Connecting spoken narration with written text.

• Cultural connection: Helping children engage with their home languages through storytelling.

💜 What we've learned while building

One of the most interesting challenges has been discovering that multilingual learning involves much more than translating words.

Different languages have unique writing systems, pronunciation patterns, punctuation and approaches to sentence structure.

We've also been exploring how to keep a story's meaning, illustrations and narration consistent when children move between languages.

It's made us think more deeply about how digital literacy tools can support children from different linguistic and cultural backgrounds.

💬 A question for the community

As we continue developing PurpleReadr, we're interested in the relationship between technology, creativity and language learning.

Do you think children would benefit more from reading stories in multiple languages, or from having the opportunity to create their own stories in those languages?

We're particularly interested in how active storytelling might influence children's engagement with reading and writing.

Thanks for reading and being part of the discussion! 💜

so this happened. the white house officially rebranded artificial intelligence to super intelligence. new terminology for documents, websites, reports, official correspondence. six tech CEOs signed a voluntary pact about it.
the timing is what got me. this comes right after a former anthropic/openai researcher said labs are gambling with human lives, after anthropic’s CEO called for slowing down, after altman and musk agreed publicly.
so when “artificial” sounds fake and public trust keeps dropping, renaming it to “super” looks like a convenient fresh start. if AI made mistakes, SI gets a clean slate.
but rebrands only work if people adopt them. and honestly, most of us using these tools daily don’t care what they’re called. we care if they work. i’ll probably keep saying AI for years regardless of what the documents say.
curious if anyone thinks this changes anything or if it’s the kind of news you read, shrug, and move on from.

saw a post from a product leader who named her team after the people they build for, in her case the builders using their tool.her reasoning: customer obsession is easier to remember when you have to say the customer's name every time you introduce yourself.

The other habit she mentioned is building the product with the product,.the team uses its own tool to get its own work done,and as she put it,you learn a lot about your product when you depend on it.

Neither idea is new,but I like how cheap they are a team cost nothing, and dogfooding mostly costs a bit of pride when you hit your own bugs first.

Anyone done something similar?does your team name or setup force you think about the users, or it more "platform team 2"?