piterrro

27 commits, my gosh, some people are really bold in their claims.

> I built the same and open sourced it

How can I *trust* you its true? Will you bet your money its __the same__ ?

I always start with a plan - this is the time where the model can leverage existing AGENTS.md scattered across every level of the code, take a look at test cases and written product documentation commited to repo. Once the "design doc" looks ok, I ask for implementation as a PR - when its ready, all CI checks should be green and I get a summary of what was done and whether anything was missed.

I can ask for a review from another agent (they often find NITs) if I'm hesitating.

Totally makes sense, for me it was just that I observed its doing good work so that I never really need anything stronger.

btw. I'm not reviewing code in detail anymore. I only give direction and provide architecture design vision, its the model and couple of async agents working in the background and consuming work resulting in PRs which I manually approve after an eye-glance.

I'm still using DS-flash - it beats frontier model on speed and cost keeping good enough code quality. It depends on the harness and constraints you give to the model but for most of the tasks its a great fit, I have not switched from DS since couple of weeks already (previously I would switch to Opus for some heavier work but I stopped recently).

My harness consists of multi-level AGENTS.md and SOUL.md containing "grug brain developer" and "asd-ste100" type of speech for clearer communication.

Lots of folks here say Cerebras serves outdated models.

Here’s my view: lets assume the above is true and the average serve with a delay of 6-9 months. Given that, its fair to expect them to start serving DS flash v4, glm 5.3, mimo 2.6 pro or kimi 3.1 pro in a matter of months. DS flash is already fast to the point I’ve became a bottleneck. But my point is: Cerebras should soon release better models that are not that far from frontier ones, at a fraction of cost and with multiples of the speed.

Just be patient and plan for the future.

My setup is Hermes on a beefy machine with running a swarm of pi.devs - connects to GH issues via bot app so I can call it @xxxx in the issue. It can create PR, run actions and all. If I want to see the code I just run kilo code in VS locally.

This setup is so plain and simple that it just works, and it works wonders. I feel like I’ve been running a dev team who actually communicates through issuess and keeps it up to date!

I dont get why they bother with DNS and subdomains when they could generate a unique url. This would eliminate the possibility of easy reverse dns lookup.

you buddy, just gave ai labs a great material to work on :) anything that LLMs currently struggle with but have a defined way of solving (PL are that since they are compiled and executed by a CPU) is gold for them.

I bet my money on it that somebody with reinforcement learning skills could post train a Qwen3.8 to actually write this lang

> Sundar Pichai has an estimated net worth of at least $1.0 billion*, as of Oct. 14, 2026. They own 2.2 million shares of GOOGL stock. They have sold 1.4 million shares of GOOGL stock since 2021, for an estimated $231.4 million.

Anthropic is pre IPO, so this is comparing apples to oranges.

After reading the article - no, I could not have invented transformers.

> The same reference app (HTTP service + worker + cron + Postgres), smallest tier, all three environments, live prices via Infracost:

Hetnzer server for 25usd/month and you have plenty more space to do more stuff and scale

I think the whole "runtime for AI agents" will split into multiple categories - each chosen by the agent at run time, ideally from a single provider. I'm working on an agent harness currently and realized: The agent doesn't need a full blown Linux environment all the time - often times all it needs is maybe some custom tool/script written in python/js. Having said that, providing a VM like QuickJS for running simple code could be enough (thus also cheaper). True the real savings will show up at scale BUT, I feel in couple of months, we'll start to feel scarcity of CPU and RAM in datacenters, just like we feel it with GPUs. Imagine, in 5 years from now, pretty much everyone will be using agents, agents will generate most of the internet traffic at 10 fold scale we use the internet right now. That workflow needs CPU and RAM, not GPUs.

Then being able to run small scripts in a VM that doesn't need operating system will not be "nice" it will be essential.