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That should be the ideal of everyone who demands certainty, and if you refuse to do it, because you “have enough of a certainty to know how things would go”, then don’t pretend you demand certainty, you only demanded reason to preemptively quit, because every prediction is based on a methodology, and the predictive power is relative to what the model quantifies over….I hope that albeit my obscure and at moments disconnected writing style, you understood what needs to be understood, that the inf
And you can interact wich each other sessions, it's pretty useful instead of sharing a screen, copying a prompt, etc.The hardest part was building the cloud infrastructure, trying to make it scalable, and to manage the sessions to make the best experience possible: when the VM shuts down, it snapshots everything and restores everything when you come back, even the conversation with your terminal, it's stored with a system that i built like obsidian wich also make you save a little bit
Hello again! Since my last post about Ullis, the project has gone through several changes as I was searching for the right architecture. As it turned out, KAN and Hyena were quite resource-heavy, and I couldn't get anything viable out of them. Then I tried RWKV, specifically the latest RWKV-8 Heron version with 1-bit ROSA activation. This ultimately proved to be the most viable architecture of all. As an example: on my 2020 MacBook Pro M1 with 8GB RAM and a 68GB/s memory bandwidth, I managed to start training a model with ~272 million parameters, a 2048 context length, and 32 layers—all within a 1.5GB memory footprint. On my specific hardware, this is still a highly taxing task. Due to the low bandwidth, the total latency per step is around half a minute, and the throughput drops to about 70 tokens per second. To be clear right away: I had to keep ROSA SAM on the CPU because it proved to be more efficient for these tasks than the GPU. Here are the logs as proof:<p>cargo run --release -- train \ --data data/ullis_dataset.jsonl \ --run runs/hard \ --config train_config.json \ --steps 20000 \ --learning-rate 0.005 \ --checkpoint-every 500 \ --bpe-train-mib 150<p>ullis: token stream compacted to u16 (156 MiB) ullis: compiling Metal shaders ullis: Metal ready in 0.2s ullis: Metal train: LN/QKV/CMix/head on GPU; ROSA SAM on CPU (~603979776 bytes idx+y+out/step) ullis: starting loop after 379.0s of setup ullis: clipped SGD lr=0.005 rosa_grad=StopGradBits (QKV frozen; window-mean CE on FP16, token-sum STE on BinaryConnect; |w0|=0.01) step 1/20000 loss=7.9256 ema=7.9256 p10=5.218 p50=8.193 p90=10.197 unigram=5.958 unique=597 n=1389 flips=0/0/0 (head/cmix/o) embed_grms=6.40e-5 scale_grms=8.09e-3 cmix_vrms=0.013 |w|=0.010 dw=4.37e-6 bias_rms=2.033 resid=3.21e-8 rss=1643MiB 27881ms 73 tok/s ullis: step 1 phases embed=8ms fwd_ln=370ms fwd_rosa=7220ms fwd_cmix=5897ms head=1070ms bwd_ln=625ms bwd_cmix=9754ms bwd_rosa=2678ms embed_sgd=57ms step 2/20000 loss=7.0537 ema=7.8384 p10=1.670 p50=7.518 p90=9.969 unigram=5.564 unique=590 n=1520 flips=0/0/0 (head/cmix/o) embed_grms=6.46e-5 scale_grms=1.06e-2 cmix_vrms=0.013 |w|=0.010 dw=4.68e-6 bias_rms=2.033 resid=5.67e-8 rss=1628MiB 27997ms 73 tok/s ullis: step 2 phases embed=8ms fwd_ln=314ms fwd_rosa=5717ms fwd_cmix=4738ms head=1231ms bwd_ln=786ms bwd_cmix=11755ms bwd_rosa=3180ms embed_sgd=60ms<p>With scaled-down settings, training runs with pretty satisfactory performance. Here is an example: since a multi-stage training pipeline with pre-training is not implemented yet, I trained it on a large Claude Opus distillation dataset. After 5500 steps, here is the result:<p>cargo run --release -- generate \ --checkpoint runs/ullis_b1_32m/checkpoint.safetensors \ --prompt "2+2" \ --temperature 0.4 \ --top-p 0.8<p><pre><code> Finished `release` profile [optimized] target(s) in 0.60s Running `target/release/ullis generate --checkpoint runs/ullis_b1_32m/checkpoint.safetensors --prompt 2+2 --temperature 0.4 --top-p 0.8`</code></pre> - *No a single = = = = = = i < (r: $=3_x(d: $d = \frac{[b_t + 2>[b_t + 1 + 3-c)$<p>- The correct answer is a list of the number of the first n > 0<p>As you can see, the model is capable of learning, but since I cannot afford to keep my main work laptop running training tasks 24/7, I had to stop at this relatively modest result. There is, of course, a possibility that some training algorithms might have implementation bugs, but this is the current outcome. I hope you find this project interesting. Honestly, pulling off a project like this entirely on your own is quite tough, especially considering I'm developing it alongside an LLM assistant (which constantly tries to break things). I'm not deeply experienced in this field yet, so I'd be incredibly glad to find testers, people with domain expertise, or anyone willing to share any kind of feedback. Thank you!<p>Link: <a href="https://github.com/Vladislav-Kalinkin/ullis" rel="nofollow">https://github.com/Vladislav-Kalinkin/ullis</a>
I built a tool (<a href="https://1mil.app/hn" rel="nofollow">https://1mil.app/hn</a>) to help me test my intuition about various weird business ideas that were on my mind. Initially, I wanted to create a database with 1 million business ideas (hence the name - "1 million opportunities"). But turned out to be a dumb idea already. So instead I built this tool that returns only 10 ideas. It's all based on your input, market + your background.<p>The first release scored on demand only and it gave false confidence. So I rebuilt the the scoring around "winnability": can a solo founder with a specific background "win" against what already exists?<p>Heres what still bugs me: since winnability went live in June, it has scored 500+ ideas, and none reached 7/10. The best was 6.3 and the median is 1.3. That includes every scan I ran for myself.<p>My preliminary conclusion: most business ideas are improbable to win.<p>Well, go ahead and try the scanner, and let me know what you think. A free scan without an account is included. Each scan costs Vertex tokens, which is why the tool isn't free.
We’ve been making project-management software for architects and engineers since 2023, and are now experimenting with a second product.Cogram Studio (studio.cogram.com) is a CAD and BIM workspace for AI agents to create three-dimensional models and dimensioned drawings.Studio runs FreeCAD (https://github.com/FreeCAD/FreeCAD) 1.1 headlessly, using the OpenCASCADE (https://github.com/Open-Cascade-SAS/OCCT) geometry kernel. It imports and exports STEP, IFC, S
Hello. I developed an open source tool called BentoPDF. Its an open source PDF toolkit that runs in your browser. With the latest update, you can actually edit existing pdf text, images and objects right in your browser.<p>Live Website: <a href="https://www.bentopdf.com" rel="nofollow">https://www.bentopdf.com</a> Repository: <a href="https://github.com/alam00000/bentopdf" rel="nofollow">https://github.com/alam00000/bentopdf</a><p>Along with the latest update I would like to share with you Hyper Compress. Its a high fidelity, content preserving compression engine that preserves PDF conformance and surpasses all the other open source PDF compression tools.<p>It runs everywhere: CLI, Node SDK, C API, self hosted service, and also in browser via WebAssembly.<p>Live Website: <a href="https://hyper.bentopdf.com" rel="nofollow">https://hyper.bentopdf.com</a> Repository and benchmarks: <a href="https://github.com/alam00000/bentopdf-hyper-compress" rel="nofollow">https://github.com/alam00000/bentopdf-hyper-compress</a><p>Kura is a PDF standards, conversion and preflight engine.<p>It supports:<p>- All 11 PDF/A conformance levels: PDF/A-1a, PDF/A-1b, PDF/A-2a, PDF/A-2b, PDF/A-2u, PDF/A-3a, PDF/A-3b, PDF/A-3u, PDF/A-4, PDF/A-4e and PDF/A-4f<p>- Accessibility: PDF/UA-1 and PDF/UA-2<p>- Print production: PDF/X-1a, PDF/X-3, PDF/X-4, PDF/X-4p, PDF/X-5g, PDF/X-5n and PDF/X-5pg<p>- Engineering and variable data printing: PDF/E-1 and PDF/VT<p>- E-invoices: Factur-X, ZUGFeRD, XRechnung and Order-X<p>- 396 bundled print-preflight profiles<p>It has been tested against several standards suites, including the veraPDF corpus, Isartor, BFO, Ghent Output Suite 5.0, the PDF/UA Reference Suite and Cal Poly's PDF/VT suite.<p>Across 30,677 PDF conversions it had zero crashes and zero timeouts, with a 0.05 second median conversion time.<p>Like Hyper, it ships as a CLI, C library, npm package, Docker image and WebAssembly build.<p>Live Website: <a href="https://kura.bentopdf.com/" rel="nofollow">https://kura.bentopdf.com/</a> Repository and benchmarks: <a href="https://github.com/alam00000/bentopdf-kura" rel="nofollow">https://github.com/alam00000/bentopdf-kura</a><p>Both Kura and Hyper Compress are running the WASM build so none of your PDF is uploaded and everything runs in your browserr<p>If possible I would like you guys to try them out and give me feedback on how it worked for you.<p>Thank you.
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