Genuine question: can these posts be paid to hype the open source models? If yes, what would be the purpose?
On my work tasks, FastAPI Python and Springboot Java on a modern SaaS product, the only open model that can do tasks well and efficiently is Qwen3.7-Max.
In all my experiments, both GLM-5.2 and Kimi are busy grepping around the codebase for ALMOST 70-80K tokens before writing anything and when they do it typically breaks the code… it feels to me that these models are good but only when you write out a super detailed spec of the task just like it was done a year ago… Qwen3.7 just… does it
I hope you're right and I want you to be right, but, even seeing the current hype around local models, etc... and open-source models, I think the industry is currently under a big confusion where they see the benchmarks of things like Kimi, GLM, Qwen, they play with it via opencode, and they think like: "Wow this is pretty good, I want to deploy this". But they don't understand how the KV cache grows over time and can take almost as much memory as needed for a 30B param model, they dont understand that a quantized model WILL NOT be the same as a full precision one, and they surely don't see the engineering work needed to serve inference to even tens of customers at a decent quality and latency level.
The biggest moat of these giant labs and models is increasingly shifting towards deployment capabilities and (debatably) having better (proprietary) harnesses.
The models themselves can be impressive on benchmarks, but unless they can be served reliably to customers either at scale, hosted somewhere, or even on edge with predictable latency and memory usage, then frontier will always be leading.
There's nothing practical about open-source models yet that makes them even remotely comparable to closed frontier models.
All the hype around GLM, Qwen, now Kimi.... Are people really this naive that they believe these reports or, more worringly, are people NOT using these models and seeing the HUGE gap that still exists?
Take a task, any medium-sized task, decently scoped that you'd trust to give to Sonnet to finish without a hitch. Now give it to ANY open-source frontier model and watch them struggle and go in circles while failing tool calls and randomly assuming things.
Open-source is and has been amazing but its so hard to deploy reliably and at scale and there's still big problems in the underlying models with instruction following and tool calling that makes it basically unusable for production workloads at a decent price point...
Delivery is becoming a sequence of checks, not a ritual
The same thing happens when it is time to deliver.
First, I invoke my /definition-of-done skill. It checks whether the implementation covers what was stated in the PR and in the plan. It checks tests and the other validations I care about. It tells me when something looks unusual, broken, or missing. When everything looks good, I invoke another skill, /pr-check-release. That checks the remote PR, updates labels, removes [WIP], adds [RFC], updates the description, and prepares the change for review. If one day passes and nobody on the team reviews the PR, we merge it. The agent also tracks that condition for me, so I can run /pr-merge-dev, and it takes care of the process: merge the PR, delete the remote branch, delete the local branch, and pull dev back into a fresh state. None of these steps is particularly difficult. That is exactly the point.
But then:
I still care about all of those things. Probably more than most people.
Obviously the author has irreversibly became AI-pilled and the day API costs balloon or APIs are down, what work will the author do?
I love using AI but please read the diffs and process them with your human brains and eyes. Spin up your containers manually, test the app, MANUALLY.
Talk to real users face to face.
Outsourcing the grunt work is fine, but there's a fine line between that and becoming a button-presser.
I only wish Opus 4.6 from earlier this year at a faster inference speed.
Since Opus 4.6 things have been so much messier and the overall push for more agency isn’t really panning out for agent assisted development as much as they would like
Thanks for saying this! I completely agree with everything you said!
There’s far, far too many people who confuse code quality for speed of development and start treating code quality as the product for customer base in the hundreds and active customers in the dozens and for most features to be basically unused.
The reality is that tech debt as a concept these days is hardly real: to be in debt means previous decisions or a previous implementation makes current work extremely hard or impossible, but, the truth is that the human factors such as knowing what to build, team collaboration and even speaking to customers matter far more and can get you “in debt” so so much faster than code alone. At least in your typical SaaS company.
If you ship code in a way that you let tech debt pile up to the point that customers notice it, you have an organisational problem, not code issues per se.
The fact that a lot of people don’t get this is really baffling to me.
I’ve been plugging away at my running coach style app, powered by the original idea of training for a trail race while living in a flat area with no easy access to natural climbing that has evolved into a fully functional plan generator: https://runcoach.fly.dev
It works well for me so far and I’m pretty happy with it!
Side question: I've always been a recreational runner, running 3/4x a week, completed a few half marathons, and recently decided to _also_ go to the gym to do strength training as it has a lot of benefits for runners too. Should I consider/take creatine, is it useful?
What if you then use AI to try and maintain only one, a single product into which you’ll put your care and craft to try to make something that’s better than “some dopamine hits”?
Any reason why they indexed on Kimi K2.5 model? I have tried many open-source ones in Opencode, and, in my experience (standard backend development, Java, Python, Spring, etc) Qwen3.6 is SO MUCH BETTER that's shocking. Kimi can't even get most tool calling arguments right.
I still haven’t seen any other models be as complete as Claude inside Claude Code. I bet Anthropic knows this and they turn the knobs and see people’s reactions… I have been planning with Qwen3.6 Max inside opencode, absolutely game changer.
Opus can then follow the plan quite detailed and like this I can make progress on my toy apps on Pro plan at 20/mo.
For work, unlimited usage via Bedrock.
Yes I’d like to get more usage out of my personal sub, but at 20/mo no complains
I’ve been using Opus 4.6 extensively inside Claude Code via AWS Bedrock with max effort for a few months now (since release).
I’ve found a good “personal harness” and way of working with it in such a way that I can easily complete self contained tasks in my Java codebase with ease.
Now idk if it’s just me or anything else changed, but, in the last 4/5 days, the quality of the output of Opus 4.6 with max effort has been ON ANOTHER LEVEL.
ABSOLUTELY AMAZING! It seems to reason deeper, verifies the work with tests more often, and I even think that it compacted the conversations more effectively and often. Somehow even the quality of the English “text” in the output felt definitely superior. More crisp, using diagrams and analogies to explain things in a way that it completely blew me away. I can’t explain it but this was absolutely real for me.
I’d say that I can measure it quite accurately because I’ve kept my harness and scope of tasks and way of prompting exactly the same, so something TRULY shifted.
I wish I could get some empirical evidence of this from others or a confirmation from Boris…. But ISTG these last few days felt absolutely incredible.
In the end, Anthropic is a company and needs to make money, my best bet is that even those of us who pay 100/mo to use Claude Code are costing Anthropic money, besides all the rest they’re burning on inference.
Again, I agree with you and the service should be at least reliable but to be completely fair, if I had to bet, the amount of usage people get for 100/mo is probably only balanced out by the corporate/entreprise customers paying their bill to Anthropic via API usage.
If we look at it through this lens, this limits are not surprising at all, except maybe on how generous they are/were. It’s pretty obvious that they want to force people to pay as they go….
Ah yes this is sad to see and a lame move for sure… It’s indeed dependent on usage hours but it’s a bad move even if I’m personally not affected since I use it outside of those hours but I agree it’s lame….
I think in this case, we probably have different experiences that shape how we see some things differently: I see many (very smart) people doing certain things that are not optimal (eg: copy-paste entire files instead of referencing them or tell claude at every message to "read CLAUDE.md and follow its instructions precisely") which can lead to a lot of token waste. If certain system prompts were tweaked internally or some models now read more files than before, keeping these "inneficient prompts" will make limits exhaust faster. Sub-agents or this new agent teams feature didn't exist until a few months ago: that alone eats A LOT of tokens, not intended for this pre-paid API usage, etc.
The ecosystem is evolving super quickly so, our own experiences and workflows must keep adapting with it to experiment, find limitations and arrive at the "tightest possible scope" that still allows you to get things done, because it is possible.
Another example: pre-paid monthly subscription aggregates usage towards web and Claude Code, for eg. So if you're checking for holiday itineraries over your lunch break, then decide to sit down and ask a team of agents to refactor a giant codebase with hundreds or thousands of files, context will be exhuasted quickly, etc, etc.
I see this "context economy" as a new way of managing your "mental models": every token counts, and every token must bear its weight for the task at hand, otherwise, I'm "wasting budget". I am also still learning how to operate in this new way of doing things, and, while there have been genuine issues with Claude Code, not every single issue that people encounter is an upstream problem.
On my work tasks, FastAPI Python and Springboot Java on a modern SaaS product, the only open model that can do tasks well and efficiently is Qwen3.7-Max.
In all my experiments, both GLM-5.2 and Kimi are busy grepping around the codebase for ALMOST 70-80K tokens before writing anything and when they do it typically breaks the code… it feels to me that these models are good but only when you write out a super detailed spec of the task just like it was done a year ago… Qwen3.7 just… does it