CompilingThings retweeted
I'm available immediately for fractional B2B roles:
• DevRel — I build real-world demo funnels around your product, aimed both at the general public and at the audience I know best: engineers. Not toy demos, but working applications that make the product's value obvious and give people a reason to try it.
• Technical Product Advisor — I help cut, reshape and redefine product features from a deep engineering perspective: what should exist, what should disappear, what is overcomplicated, and what can actually be shipped.
CompilingThings retweeted
Anthropic is going to release Fable 5.5
The timing of them giving away 20% of shares is not an accident.
Opus 5.5 is already doing 10x the work of an average human at my job.
Fable 5.5 will take it to the next level.
Claude has been saying for years that white collar jobs are going away.
Anthropic is now ear marking money for the job loss.
CompilingThings retweeted
Gyro-S on a RX 9070, 16 GB and 32GB ram. This shouldn't fit.
- 1,100+ tok/s prefill, ~60 tok/s decode
- 5/5 multi-module Java builds green
- 0 loops
- 262K context + vision in 25 GB RAM
Qwen3.8-Flash-Next, 2-bit, running on Strata
huggingface.co/agentionai/Qw…
CompilingThings retweeted
Never give API keys to some random Open/~ apps.
Just use my ⚡️SUPER DESKTOP from you lovely home lab. Thanks to mobile clients you can access any of your harness without leaking. Make sure to secure tailscale.
superdesktop.dmytrogladkyi.c…
No accounts. No API. No bullshit.
CompilingThings retweeted
Intel GPU D-series, code-named Crescent Island
- 480 GB LPDDR5X memory
- 1.5 TB/s of memory bandwidth
- 1.3 petaflops of FP8
- 350 W TDP
hardwareluxx.de/index.php/ne…
CompilingThings retweeted
Omandroid: work-in-progress update 🧪📱
Native Omarchy running on a Pixel 7 Pro: Arch Linux ARM, Hyprland and Omarchy's own packages, on a mainline Linux kernel. No Android underneath.
Hardware working
- Boots our own mainline kernel (7.3-rc) from the second boot slot, with stock Android kept on the other slot
- Display (1440×3120 OLED)
- GPU: Mali-G710 with OpenGL ES 3.1 and Vulkan 1.4 through Mesa (we added the G710 to Mesa's Panfrost driver), and Hyprland renders on it
- Touchscreen with multi-touch
- Power and volume buttons
- Battery level in the status bar
- Internal storage, with the system partitions locked read-only
- USB networking and SSH
Software working
- Omarchy themes, the menu, the notification shade and the Omarchy boot animation
- A phone-style home screen and launcher with app search and "ask your agent"
- On-screen keyboard
- App switcher: swipe up, tap to switch, flick to close
- Lock screen with a PIN pad: locks after boot and after the screensaver
- Power + volume down takes a screenshot
- Apps open full screen, like on Android
- Kernel updates installed from inside Linux
Next up: Wi-Fi (driver in progress), Bluetooth, charging, audio, then camera, cellular and Android apps through Waydroid.
Shepr for omarchy for herdr
Agents wait on my answer to a question while im walking Freya...
So I built Shepr ...an iPhone home screen app that shows what's waiting on me. Send photos with comments, answer multi-choice AUQ's and open existing project folders as a new titled herdr projects.
It has your 'Subscription' information that reports sub token usage and Alerts in Settings. Knowing if you are on pace, behind or have banked tokens is super useful...lets you know when you can burn them gpus!
What's Shepr missing? Tell me what you want on your phone and I'll add it... or go fork yourself! :)
github.com/nixfred/shepr
CompilingThings retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
CompilingThings retweeted
After @no_stp_on_snek updates to ROCmFPX v2 GLM 5.3 Flash (3.66 BPW custom quant) not only improved in speed but Human Eval and Human Eval+ Results scored high.
@AIatAMD x @FrameworkPuter Gorgon Desktop 192 GB
CompilingThings retweeted
We turned @OpenAI's math problems into open-source RL environments on @huggingface!
Early and rough (the verifier only accepts the exact original formalization, so an equivalent proof can still score 0), but this is what it looks like when a research release becomes executable training infrastructure for everyone.
This is an exciting direction imo: turning open research into open executable environments that anyone can build on to train better open models!
huggingface.co/datasets/Fine…
CompilingThings retweeted
Updated Qwen Flash Next Gyro-s with even higher precision and even better resistance against looping.
This is a great quant at only 29GB.🔥
Fits fully in a 32GB GPU.
220t/s on a single 5090. 🚀
It runs on my forks of Strata and llama.cpp.
More info in the model card.
All my small quants undergo my custom 45 prompt loop test and Gyro-s passed with flying colors unlike most other small quants I tested.
huggingface.co/agentionai/Qw…
CompilingThings retweeted
Play stupid games, win stupid prizes.
github.com/obs-rust
CompilingThings retweeted
If your a teenager building something, Anthropic just made it a lot easier.
Their Claude startup program gives you:
- A free year of Claude Team
- $1,000 in API credits
- Up to $45K in partner discounts
- Scheduled calls with their AI team
All you have to do is describe your project and apply.
+ plus Claude is the best AI tool on the market right now.
Vulkan, Metal and Cuda all working together via MCDMA and TensorFold.
Thank you Petrus for helping with all of this!
All hardware should join in to run your local AI! With maximum efficiency.
That's why we are building Tensorfold with @ashxhart and his team.
Here's a little video of my homelab architecture with explanation of two features which make Tensorfold fast.
CompilingThings retweeted
Apple locks fast local AI to macOS with Metal.
I picked the lock.
metal2vk translates Metal GPU code to Vulkan, unchanged.
598 of 610 real programs from @trymirai's uzu and @ashxhart's TensorFold so far.
Engines built for Macs now run on Linux.
github.com/joshuaswarren/met…
CompilingThings retweeted
Something I keep hearing from people running dev teams: "we pay for Claude, Cursor, Copilot... and honestly no idea where the money goes."
I had the same problem myself, so I built CodeBurn. It's free, it runs on your laptop, and it just shows you what your AI coding actually costs. Which project, which model, what's wasting money. A lot of people use it now, which still surprises me.
Now I'm working on a team version. The idea is simple: if you're paying for these tools for your team, you should be able to see if it's worth it.
I'd love to build it together with a few real teams, not guess what people need. If you run a small team and this sounds useful, sign up at codeburn.app/teams or just drop me a message. Happy to talk.
And if you just want to see your own numbers: npx codeburn
CompilingThings retweeted
Update: 209 tokens per second for Qwen Flash Next on a single 5090. 🚀
221 t/s for JSON generation.
It took some kernel tuning and there is potentially more space for improvement. This ran on an unreleased version of strata.
Llamacpp did nearly as well.
Looking forward to release this with @coldniko in the coming days
CompilingThings retweeted
We're growing 🪴
Join us:
xitter.cf/i/chat/group_join/g209…

ALT Model: Krea 2 Turbo + bypass LoRA Prompt: Watercolor and ink illustration of a mechanical 5 gear system of various sizes small and big, intertwined with organic growing vines and leaves, symbolizing open source hardware and software working together, soft muted earth tones with pops of AMD red, AMD logo in the middle gear, white paper texture background, editorial illustration style





























