Get started → https://t.co/GSQnqBWwty
Why create the GOAT plan?
At Command Code, we're incessantly curious about 1) open models, 2) how to get the best value out of them, and 3) how to make that value accessible to everyone. The GOAT plan is our answer to all three.
While almost every other coding agent was built to serve closed models, we built Command Code to be the best coding agent harness for open models.
We started with the $1 Go plan. The sheer audacity of that plan was a hit, but it was also a bit of a tease: a great way to get started, not enough usage to last the month.
The GOAT plan fixes that - it's the best value in the coding market today, with enough credits to do something meaningful with open models.
Coding with open models comes with hard problems - cost, reliability, safety, and the sheer complexity of running them well. We set out to solve all of it, so you can pick an open model and just code.
From getting DeepSeek to beat Opus, to building infra with leading ~98% cache hit rates, to fixing AI slop with the built-in /design skill - Command Code has made its way to the top of the open model coding world, everywhere. The GOAT plan is the next step in that journey: open models, accessible to everyone.
Open models are already beating frontier closed models at coding. How rich is this moment if you think of it.
With the v1 release, which is a full rewrite of this 6-year-old codebase, it's now arguably one of the best, most mature coding agent harnesses. Check out the new Mods API - you can build anything you can imagine.
We can't wait to see what you build with Command Code.
We're also open sourcing Command Code later this month.
This is exactly right. Source code is on the verge of becoming like assembly.
The next step is getting rid of “source code” entirely and just making an efficient binary directly with AI.
I might never look the source again. It was nearly a half century ago that I stopped looking at assembly because I trusted the compiler to get it right. This feels like that.
SiFive's P870-D is ideal for datacenter infrastructure applications, including storage, web servers and video streaming. It's our first Datacenter class CPU IP, and it's already in real silicon with our customers.
Deploy it standalone or alongside SiFive Intelligence cores to level up your compute. We also provide the uncore agents, system IPs like security and RAS.
👉 Download our P870-D Product Brief below or click the link to learn more. → https://t.co/pXCsJm5b90
#Semiconductors #RISCV #Datacenter #Hardware
🚨 BREAKING
We're on Hacker News again 🧇
we figured out how to serve Kimi K3 at 3.8x higher throughput and 71% lower cost on @AMD MI355x vs B200
more here: https://t.co/jHnWGkWAVo
While DeepSeek V4-Flash is significantly cheaper on price per token, this can be misleading if the overall cost per task ends up being higher due to more turns being made.
However, @ArtificialAnlys reports DeepSeek completing the same benchmark tasks as Fable at 105x lower cost.
Concrete [CONtinuous and disCRETE] Mathematics — A Foundation for Computer Science (and for serious users of mathematics in virtually every discipline): https://t.co/uB0fHTXaGU [672 pages]
Topics covered:
🔶 Evaluating Horrendous Sums
🔶 Recurrences
🔶 Integer functions
🔶 Elementary number theory
🔶 Binomial coefficients
🔶 Generating functions
🔶 Discrete probability
🔶 Asymptotic methods
GPT-5.6 Terra and Luna are 50% off in Command Code.
Get 2X requests for every dollar of credit you spend.
GPT-5.6 Luna:
- $0.10/M input, $0.60/M output tokens
- Available on all plans (Go, Pro, Max, Team)
GPT-5.6 Terra:
- $1/M input, $6/M output tokens
- Available on Pro, Max, Team
For limited time only, grab before it ends.
6.2M tokens. $0.17.
This is GLM 5.2, one of the best coding models available today.
At InferX, we’re focused on making state-of-the-art models accessible without breaking the bank.
Try it: https://t.co/7AjkqIri44
DeepSeek V4 Flash 0731 is now live on InferX.
We’re making it free to use.
We’re bringing additional GPU capacity online to keep up with demand. Until the new capacity is in place, you may occasionally notice higher latency during peak periods. Thanks for your patience while we scale.
✔️ Zero data retention
✔️ OpenAI-compatible API
Try it: https://t.co/WNGudxbt3g