The smell is about folks being attached to the same model because they're used to it. Just like any UI change in an app that one uses everyday is annoying.
What's often missing is the actual engineering. If you have evals for your use case, you use these to adjust to the new model. DSPy has great prompt engineering constructs it ships with.
The smell is all about vibing, where a model feels better because the structure of the answers is more familiar to a person, instead of engineering where given constraints and input/outputs one is using/bending the system to fulfil the requirements.
I love how there is a "Please do not discontinue gemini-2.0-flash[-lite], 2.5 is NOT an equivalent" from Feb 20th. Getting too attached to models is a smell.
There is literally no more reason to use their service. I don't need a monthly expiring token pool drained at API pricing which I can get from any provider.
Wonder if this is a product decision or rather a limitation on GPUs. Their Inf2 instances require AWS Neuron [1]. Maybe porting models to run on their hardware takes too much time.
Your other plans are fixed price with rate limits where you get more tokens than the dollar equivalent you pay monthly. These plans are economical only if majority of users spend less tokens in $ than the plan's costs. This subsidizes the gap vs. power users who spend multiple k$ monthly in API tokens.
Wonder if folks will flock to competitors that still over fixed-price review packages only for those companies to switch to usage-based pricing. They're next to be overrun with new customers and sit on the increasing bills.
What's the residual value of Copilot after the changes? For Enterprise plans, even copilot code reviews will be charged at token price + github action minutes for the execution of the review. One can roll one's own reviewer and have it spend the API tokens as well. At least one will be able to select the subset of files for the change set, if needed.
The background agents will also depreciate in value because of their harness that's a black box that's not optimized for token usage at all. Rolling one's own will be a better choice here.
"Paperasse is a collection of skills for AI agents (Claude Code, Claude Cowork, Codex, Mistral Vibe, Cursor, Windsurf, Cline, Aider) specializing in accounting, taxation, notary services and auditing of French companies."
I wish the team would be more responsive to popular issues - like inability to provide a dynamic api key helper like claude has. This one even has a PR open: https://github.com/anomalyco/opencode/issues/1302
Are there any videos or screenshots of readout? The webpage is very minimalistic.
https://github.com/dlupiak/claude-session-dashboard - I found this one today, it shows me nicely basic statistics. Do you know the similar tool that would be showing also the interactions of sub-agents? I want to see the user prompts and also statistics by file which tools have touched or read them.
The sample code is written in Elixir, however, they also release the full specs using which one can generate the application in the language of one's choice. I'm curious if this is really so straightforward.
Let's hope the component shortages will drive performance improvements in Apps as it will be unfeasible to expect higher specs to fix performance bottlenecks. Constraints can drive good behavior.
Looks like the blog could use a HN=True. Hope the author won't get banned...
> Error: API rate limit exceeded for app ID 7cc6c241b6e6762bf384. If you reach out to GitHub Support for help, please include the request ID E9FC:7BEBA:6CDB3B4:6485458:699EE247 and timestamp 2026-02-25 11:51:35 UTC. For more on scraping GitHub and how it may affect your rights, please review our Terms of Service (https://docs.github.com/en/site-policy/github-terms/github-t...).
Exactly, I've seen people who bought a Mac Mini and ended up running claw against a claude subscription. Completely misunderstand the point of local models. Plus, there was more hype about running claw way cheaper on Raspberry Pi which cost the stock price of Raspberry maker to skyrocket.
Some of the comments here show that technical people set these things up for non-technical people, which is just one step away from a misstep. Time will show whether this is similar in behavior to the "I can run it" mindset that people had with local models before. A small dopamine hit to see "it can be done" in order to end up a cloud service in the long run.
The host shenanigans are simpler addressed with `git config core.sshCommand` which overrides the default key and selects the desired one on a per repository level.
What's often missing is the actual engineering. If you have evals for your use case, you use these to adjust to the new model. DSPy has great prompt engineering constructs it ships with.
The smell is all about vibing, where a model feels better because the structure of the answers is more familiar to a person, instead of engineering where given constraints and input/outputs one is using/bending the system to fulfil the requirements.