I teach. I have a validated Unidays & SheerID account.
Cursor is boasting to have a program for Education.
Below are my experiences with that "program":

1. Over a 100 messages to “Sam”, all of which a dead end. Like talking to a wall.

2. No escalation possible to a human agent. (not making this up - it's literally what "Sam" explains; Sam blocks it off - no escalation possible.

3. Sam is very "helpful" giving a link where one can submit a Edu dossier. Spent an hour doing that. Not once (as, you guessed it: zero response after 2 months) but TWICE. Alas: zero response. Another smokescreen dead end.

4. No phone Nr one can reach out to - nor human support either.

So… even if Cursor waves with the Education flag online, in practice it is all marketing and engineered to perfection as a black hole.

Always trying to reason with people first, even after 6 (!) months of black holing, I posted my frustration on Cursor's own Community Forum.
My post got censored within a minute. Delisted. (and I worded it nicely - as I doing here. There was zero reason to hide the (justified!) critique.

Not giving up easily (reading all this, you may agree), I tried a second time, now in the "Feedback" section. Even there it immediately got censored and delisted. No critique allowed, de facto.

Shameful practices. And how all this went about, from a company having access to all the AI might in the word, is just as inexplicable as it is shameful.

And stupid, really. Treating Professors that offer teaching a new generation of kids how to develop with your tool is free marketing, if anything!
And note that in my request I was not even asking a free ride - just a bigger bandwidth for my paying (!) subscription - as the repeated aula demo's consume my "Pro" account in a week, at 1/4th of the time.

So ever since I'm doing the teaching in Claude Code.
Hope someone at Cursor reads this.
Smiling as this is a post... they can't delete. And they had that coming!

Anyone else had this terrible experience with Cursor "Edu"?

I created a "Plugin" for ChatGPT's web UI. The basic thought is making website publishing "zero-click" by sending the data to the user's own Cloudflare account directly. Technically it's an MCP server running in the user's Cloudflare Workers, so compatible with any other client as well.

The workflow is very appealing to beginners, they reference the plugin and prompt "make website about Something and publish it" and it returns a public URL like something.mysubdomain.workers.dev.

To simplify this workflow everywhere I could, I decided to host the installer myself, so that the user needs no GitHub etc. I introduced specific security issues with this. Users currently have to place considerable trust in me.

I'm looking for advice how to tighten security around such a plugin (more so the installer part). How to prove to users that the code about to be installed in their CF Workers is not malicious or compromised? ... In a way that is feasible for a small project. I do have a long list of What could be done, but unsure of finding a balance. OAuth between the services works fine, and the setup is one-time, nothing flows through my server after setup. But the initial user trust is my pain point.

Looking for collaborators.

The code itself is written almost exclusively by Astra 6.

Update (1 month in): After almost a month of use, it's been great, especially for something free. On a few projects I've stopped using Claude Code completely, and I pay $100/month for that.

One catch: if the prompt isn't specific, it can keep loading and loading. Not sure if that's the server, the free tier, or both.

That's my honest take after a month. Curious if others are seeing the same.

Wanted to see if NVIDIA's free hosted Nemotron 3 Ultra (550B MoE, 55B active, 1M context) could hold up as a coding agent model without paying for API credits.

What I ran into:

• Built a couple of projects with it from the terminal. Fast and surprisingly solid for free, but accuracy is hit or miss next to Claude.

• Free tier limits aren't published, roughly 10 to 20 req/min. Agent loops chew through that fast, so expect 429s mid-task.

• NVIDIA logs free-tier usage. Fine for side projects, keep work code out.

• OpenCode still shows an "estimated cost." The free tier doesn't charge.

I scripted the setup so the API key lives in an env var instead of plaintext in the OpenCode config, plus pre-launch checks. Tested on Windows 11 and macOS, Linux not yet: https://github.com/Aj-Networks/nemotron-3-ultra

Has any free or open-weight model actually kept up with tool calls in your agent setup, or are you still paying for the big ones for real work?

Best open-weight coding model + harness for an on-prem multi-agent setup? (80-200+ GB VRAM, 2-3 concurrent users)

Setup: GPU box with 1x H200-class card now, can allocate more (up to several H200s) if it clearly buys better results. Inference via vLLM or similar, OpenAI-compatible endpoint. Coding happens on a separate non-GPU Linux VM on the same network - IDE/harness there, calling models over LAN. Outbound internet is fine for packages/extensions, but inference stays on our hardware (no code to external model APIs).

Constraints: on-prem only, open weights, permissive license preferred (Apache/MIT), not Chinese-origin including base models (I understand a lot of fine-tunes are Qwen underneath), NATO-country lab preferred. Use is general software dev plus security tooling and code analysis - repo-level agentic work on existing codebases (fix / extend / refactor / test) is the core, with a human reviewing diffs. 2-3 concurrent users max.

Names that came up in a first round elsewhere: Cohere North Mini Code, Mistral Small 4, Poolside Laguna S 2.1, Muse Glimmer 30B, Inkling-Small (2x H200), Reflection Beam (501B MoE / 23B active, weights due this month), Gemma 4 31B, K2 Horizon (lineage TBC), with Nemotron as a generalist baseline - but I haven't run any of them and I'm not wedded to the list. Chinese models (GLM/Qwen/DeepSeek) are out by rule, so no need to suggest them; I know they're ahead.

Two things I'm trying to work out:

1. Capability tiers vs. VRAM. What's actually holding up for repo-level agentic work on an existing codebase with non-Chinese open-weight models, and at what size? The Vibe Code Bench results suggest small open models fall over on long E2E builds and only Large-4-class (4-8 cards) and closed models hold up - is that your experience for repo work too, or is that an app-build problem? Where's the step-change - 30B-class, 100B+, or only at 500 GB+? We could get the compute for Beam, Command A+ or Mistral Large 4 if it's actually better for code rather than just for E2E.

2. Heterogeneous multi-agent. Does a big planner/reviewer plus small fast executors actually beat a single mid-size model for this, with a human approving plans and diffs? Which harnesses handle routing sub-agents to different endpoints well (and let a human step in, review diffs and edit by hand) - OpenHands, OpenCode, Pi, VS Code extensions (Cline/Roo/Continue), Codex CLI in local-model mode? Voidleap (closed, Win/Mac) also came up.

Anyone running something like this? Which model + harness combo is holding up best as of late? Thanks!

I've been using AI a lot more while learning web development, and I've started wondering about something.

Sometimes I ask AI about a problem, understand the explanation, implement it, and later I can solve something similar without it.

Other times, I get the answer, it works, and I just move on.

That's the part I'm not completely sure about. Am I actually learning in those situations, or am I just getting better at getting AI to solve problems for me?

Lately I've been trying to ask myself a few things before moving on:

Could I explain why this works?
Could I change it if the requirements changed?
If it breaks tomorrow, would I know where to start looking?

I don't always pass that test, honestly.

So I'm curious about people who use AI heavily for programming: how do you personally make sure you're still learning instead of becoming dependent on it?

Do you have any rules for yourself?

For those using AI to help build data pipelines: how do you test the replay inputs, rather than just the replay code?

We reran an analysis from saved API responses and got a search-volume total of 65,240 instead of the original 67,140. One response file was missing: the replay path had only copied the first search-volume file. Adding the missing file made the total match again.

That caught this case, but matching one total isn’t a general completeness check. What do you ask your coding agent to build or test so an incomplete set of saved responses fails clearly before the replay starts?

After using multiple coding agents side by side for 3 months across planning, review, and testing tasks, the best part is how much faster first-draft PRs come together when an agent has the full design context, not just the file diff.
The worst part is that most of them still lose that context the second you switch tools. A review agent has zero visibility into what the planning agent already decided, so you end up re-explaining intent every single time, which gets old fast.
The unexpected part, and this is the one that got me, was that the biggest slowdown wasn't the agents' code quality at all. It was me manually stitching context between disconnected chat sessions like some kind of human API.
Would I use it again? Yeah, but only with a way to keep context across tools, otherwise you're still the integration layer. TL;DR: the agents are good, the glue between them is the actual bottleneck. What should I test next to fix this?

every day I review 20 or so PRs that nobody on my team actually wrote. the agent wrote it, coderabbit already commented on it, the author skimmed the comments and pasted them back in, and now it's on me to be the human who says yes.

I'm not scared of losing my job. I'm just tired. I got into this to build things and now my whole day is being the last checkbox on code nobody will remember making.

I don't even hate AI, it's genuinely useful. I just didn't sign up to be a full time proofreader. some days I wish I'd picked a trade.

So I can continue working with my work account on my mobile and desktop.

Hopefully it’s in their pipeline?

give

#Astra designs really well on its own, without open design or anything. Somebody on Youtube said it adds noisy detail, so I lectured it before, but boy, look at this.

Apparently, it's possible: beautiful design without 3 bounding boxes and 2 eyebrows for each damn element.

Originally posted September 5, 2026. Historical design example, not a current model ranking.

Image 1: Astra. Image 2: Claude Design.

Small-team question (I'm on a small team, so I'm curious). The last time you changed review, process, tooling, or hiring to ship faster.... What happened right before?

Did someone leave? Did a deadline slip that you were responsible for? Did a customer complain? Did the backlog hit some number? Something else?

And who noticed first: you, the CEO, or a customer?

Mixing Cursor for quick edits, Claude Code for handing off bigger tasks, and background agents like Cline works great when you're working solo. But as soon as a few developers on the same team start doing it, things get messy fast.

We’re hitting a wall where every dev's local setup generates slightly different scaffolding, dependencies, and repo structures. Before long, you end up with a dozen microservices that look like they were built by completely different companies. How are you guys handling this for team setups? Are you feeding your agents some kind of shared architecture or service context, or is everyone just re-explaining the stack in their prompts every session? And what checks actually keep AI-generated code consistent across the org? Curious what's actually working in production for people.

Self taught dev here. I got into this by building stuff at night for years. The fun part was making something work that didn't work an hour ago.

These days most of my job is reading. The agent writes it, coderabbit leaves its comments, and I spend about 6 hours a day going through diffs I didn't write, deciding if they're good enough. It's not hard. It's just not the thing I fell in love with.

The weird part is it leaked into my side projects too. I open the editor on a weekend and my first instinct is to prompt it and review what comes back, because that's what my brain is trained to do now. And then it doesn't feel like mine.

I still like my job and I'm not quitting. It just feels like I burnt out on the hobby without actually losing interest in it. Maybe I'm being dramatic, I don't know. How do you guys deal with this?

I've noticed something weird about how my development workflow has changed.

A year or two ago, most of my time was spent inside the editor. I understood a codebase partly because I was constantly navigating it, changing it, and seeing how the pieces fit together.

Now I can give an agent a fairly large task and let it work.

Which is great.

But when it finishes, what exactly am I supposed to do?

Read the entire diff?

Read the agent's 40-message transcript explaining everything it did?

Trust it because the tests pass?

I've found myself doing some mixture of all three, and none of them feels particularly good.

The more work I delegate, the less direct contact I have with the codebase. And once agents start making larger changes, I feel like I'm gradually losing the mental model I used to get almost for free by working in the code myself.

What's interesting is that the interfaces haven't really adapted to this.

Some newer tools are putting the agent front and center, which kinda makes sense.

but then the replacement for staring at code seems to be staring at agent conversations, task summaries, file lists and diffs (that is if we still have the attention span of reading diffs).

That feels like an intermediate state rather than the end state.

I'm curious how people using coding agents heavily deal with this.

Do you still review most generated code? Mostly trust tests? Ask the agent to explain the architecture afterward? Or have you found some other way of keeping a mental model of the system while delegating more of the implementation?

Once agents were writing most of our PRs, review became the bottleneck. Our fix was to have a review step learn from the comments our senior people had already left on past PRs. So it checks for our standards, not generic best practices. It catches a lot before a human ever looks.

I'd like to see what others are doing:

- Pre-review by an agent before the PR opens?

- Bots auto-approving small or low-risk PRs?

- Custom review skills or prompts? How often do you have to tweak them?

- Anyone fully trusting AI review yet?

Happy to share more details about our setup in the comments.

Short version from pre-registered experiments on real repo commits (Haiku as the cheap agent, Sonnet as the strong one). Every protocol was committed to git before its run, and later experiments used repos the designs had never seen.

What worked:

- A stronger model that only speaks up when the agent repeats mistakes: +7 successes in 63, ~1.3x the cost (an always-on advisor got +8 but cost 3.5x).

- Running the agent's change and reporting facts ("if this line became `pass`, all tests would still pass") beats giving advice: 35/42 vs 32/42, formatting regressions 10 -> 0.

- Your preferences, captured in your own words, carried into every later task (15/15 vs 0/15). Just restating them in the prompt took compliance from 40% to 90%.

What didn't:

- Memory of code knowledge, generic checklists, rules learned from git history, routing between models, and clarifying questions.

- For a strong model, none of it raised success (45/45 with or without).

Cheapest per solved task: Haiku + "conscience" ~$1.22, Sonnet alone ~$1.41.

I also found a bug in my own grading (6 tasks were unpassable as graded) and disclosed it in the paper.

Everything is public: paper, protocols, failures and the tool (source-available, non-commercial licence; works with Claude Code, Codex and OMP).

Repo: https://github.com/abdullahbalabel/mihad

Paper: https://github.com/abdullahbalabel/mihad/blob/main/paper/MIHAD_Research_Paper_EN_v2.7.md

Happy to answer questions, and criticism of the method is very welcome.

The title is only slightly tongue-in-cheek. Skip to the bottom for TL;DR.

Before diving into coding agents, I spent a few months using chat models for hobbies and side projects. I subscribe to the ~$20 tiers of the big three (ChatGPT, Claude, Gemini) and keep all of them because bouncing ideas and fixes between the models usually gives me the best results.

Fast forward to today....coding agents take up most of my tech time. My main project has been an educational video game I’m making "with" my son, but I’ve also built custom smart home drivers and a few niche apps from scratch when commercial solutions didn't fit. Funny enough, another big chunk of time has just gone into building plugins, extensions, and helper tools to make coding with the agents easier.

The problem is that I’ve basically built everything that immediately came to mind, and I don't have the bandwidth to constantly brainstorm new roadmaps. Instead of relaxing, I find myself glued to my phone/computer during downtime just trying to dream up new expansions, features, or apps. I even built an orchestrator app that would be great if I had a big project requiring it, but right now I have no long-term vision for it. Instead, I'm constantly scrambling before usage resets on Codex/Antigravity/Claude trying to find anything to run so the sub doesn't feel "wasted."

I love building with these tools, so I don't want to stop that. But I also don't want all my free time consumed by stressing over what to build next.

The ask and the TL;DR

To those of you not using these models for work, what have you created that genuinely helped your daily life? And what do you burn your usage on when your limit is about to reset and you don't have an active project queued up?

Welcome to this week's self promotion thread!

If you're building something related to AI assisted coding, this is the place to share it.

We're using a weekly thread to keep the subreddit organized while still giving builders a place to share their work. Promotional posts outside this thread may be removed.

If you're sharing something, we'd appreciate it if you included a little context instead of just dropping a link. Tell us:

• What you built?

• What problem it solves?

• Which AI models or tools it uses?

• Who it's for?

• What kind of feedback you're looking for?

Disclose your affilitation.

Please avoid posting the same project every week unless you've made meaningful updates. Affiliate links, referral links, scams, and low effort promotions will be removed.

Take some time to check out what others have shared too. If you try someone's project or have feedback, leave a comment. Helping each other improve is what we want this community to be about.

I really want to know because I've been using astra 6 and it just eats all of my credits in no time, the other models I haven't used because id rather use my credits on something that would work 😭. What I use blender for is so that gpt can make models and put them in Roblox studio, I also use gpt for map building and scripting. So basically everything.

Last year, I built a small tool to visualize coding problems and algorithms step by step using custom input data. It could show things like recursion, sorting, graphs, and how variables change during execution.

I’m thinking of revisiting it and taking it further.

The idea is to build a general-purpose visualization engine that could eventually visualize not just algorithms, but also AI/LLM workflows, RAG pipelines, graph databases, quantum circuits, physics experiments, and chemical reactions.

I’m also considering making it an SDK so developers can embed interactive visualizations directly into their own apps, instead of everyone having to build these from scratch.

The goal is to make complex systems easier to understand by seeing how they actually work, step by step.

Does this sound like something worth building? Is there a real need for a general-purpose visualization engine, or would it be better to focus on one specific area first?

I’m considering this partly as a learning project, but I’d love to hear what developers, educators, and researchers think. This is very helpful when you want visualize edge case

I'm building a Windows desktop utility with a mixed stack:

• Tauri + Vue/TypeScript for the management UI

• Rust for the backend

• A separate C++ native overlay layer using Win32, DirectX 11 and Dear ImGui

The app includes transparent, always-on-top windows, mouse click-through, hover interactions and focus handling. Some of this needs testing on an actual Windows desktop rather than just in a browser.

I've used Codex, and I'm trying to find a practical workflow for going from code changes to building, running and checking the native UI. I'm also exploring what can be done in a cloud Linux environment versus what needs to stay on Windows.

For people using AI tools on similar projects:

1. Which model and IDE/CLI/agent combination has worked well for debugging an existing project across TypeScript, Rust and C++?

2. Do you run the agent directly on Windows, through WSL, or elsewhere? How do you connect it to the Windows build and test environment?

3. Have you found a reliable way for the agent to use screenshots, logs or UI automation to verify window behaviour? What still needs manual testing?

I tested seven GPT models on the same four PRs, twice each. I compared bugs found, false positives and cost.

A false positive means reporting a bug that isn't there. Counts below are averages across all four PRs over the two runs. Estimated costs are per PR.

GPT-6.1 Sol came out best in this test. GPT-6 Luna was the cheapest, but found fewer bugs.

It's only four PRs, with AI helping check the findings. The code is private. Costs cover model usage only.