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LLM running on Windows 98 PC 26 year old hardware with Intel Pentium II CPU and 128MB RAM. Uses llama98.c, our custom pure C inference engine based on Andrej Karpathy llama2.c Code and DIY guide 👇

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483,223 görüntüleme • 1 yıl önce •via X (Twitter)

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This is Day 4 of The 12 Days of EXO. Blog post and DIY guide:

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On Day 3, we announced a 100% Open-Source home assistant that runs locally and is aligned with you.

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Be the first to hear what’s new by joining our email list:

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Day 5:

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Introducing our @amdgaming Powered King 95 Gaming PC. #amdadvantage

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@karpathy If it runs on 26 year old hardware, it runs on anything. GameBoy next?

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@karpathy Nice. Next try 80386 CPU (33/66 MHz), 16 GB RAM, and Xenix OS. Such a machine in the tower form factor was the server at my first job. It was a multi-user UNIX environment w/ VT100 (or VT200) green-screen dumb terminals & IMHO wonderful keyboards. Are you up for the challenge? 😉

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@karpathy This is amazing. I remember that old windows 98 startup screen

Alexander Long profil fotoğrafı

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@karpathy We really had it all🥲

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@karpathy Imagine going back in time with this in a CD and showing up to Bill Gates' office

AD profil fotoğrafı

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@karpathy The beige nostalgia with the satisfying clicks Oh yea and running LLM on a 26 year old hardware is pretty cool too

Benzer Videolar

"Somebody got one of the small versions of Llama to run on Windows 98...We could've been talking to our computers in English for the last 30 years" - Marc Andreessen 🇺🇸 It was me! I got Llama running on a Pentium II machine with 128MB RAM running Windows 98. Details below.

"Somebody got one of the small versions of Llama to run on Windows 98...We could've been talking to our computers in English for the last 30 years" - Marc Andreessen 🇺🇸 It was me! I got Llama running on a Pentium II machine with 128MB RAM running Windows 98. Details below.

Alex Cheema

766,012 görüntüleme • 1 yıl önce

Yay, llama2.c can now load and inference the Meta released models! :) E.g. here inferencing the smallest 7B model at ~3 tokens/s on 96 OMP threads on a cloud Linux box. Still just CPU, fp32, one single .c file of 500 lines: expecting ~300 tok/s tomorrow :)

Yay, llama2.c can now load and inference the Meta released models! :) E.g. here inferencing the smallest 7B model at ~3 tokens/s on 96 OMP threads on a cloud Linux box. Still just CPU, fp32, one single .c file of 500 lines: expecting ~300 tok/s tomorrow :)

Andrej Karpathy

409,734 görüntüleme • 3 yıl önce

Just become the world’s first to run an LLM locally on a business card lol. Why? For fun. Details: - llama2.c by Andrej Karpathy with modifications from Dave Bennett - 24tok/sec on esp32s3 n16r8 w/ a 260k param model - designed the card myself, whole inspiration comes from ouija boards i.e talking with ghosts. if i put llms on my business card, it ought to be the ouija form factor lol. - pcb and smt sponsored by NextPCB Dropping video on youtube + schematics this weekend

Just become the world’s first to run an LLM locally on a business card lol. Why? For fun. Details: - llama2.c by Andrej Karpathy with modifications from Dave Bennett - 24tok/sec on esp32s3 n16r8 w/ a 260k param model - designed the card myself, whole inspiration comes from ouija boards i.e talking with ghosts. if i put llms on my business card, it ought to be the ouija form factor lol. - pcb and smt sponsored by NextPCB Dropping video on youtube + schematics this weekend

Binh

385,996 görüntüleme • 1 yıl önce

Inspired by Ammaar Reshi getting C&C Generals running natively on iPad, I did the same for Warcraft II using Codex 5.6 Sol. It's the open-source Stratagus/Wargus engine compiled for ARM64, rendering through Metal via SDL2. No emulator, no x86, no streaming from a Mac. Campaigns, skirmish, and save/load all work, with touch controls. First time I've seen Warcraft II run natively on iPad. Open sourcing it all below.

Inspired by Ammaar Reshi getting C&C Generals running natively on iPad, I did the same for Warcraft II using Codex 5.6 Sol. It's the open-source Stratagus/Wargus engine compiled for ARM64, rendering through Metal via SDL2. No emulator, no x86, no streaming from a Mac. Campaigns, skirmish, and save/load all work, with touch controls. First time I've seen Warcraft II run natively on iPad. Open sourcing it all below.

Kahris

121,091 görüntüleme • 1 ay önce

locate-anything.cpp: native C++/ggml (ggml) inference for NVIDIA's LocateAnything-3B, open-vocabulary object detection / visual grounding, one of the neat detection VLMs out there, from the LocalAI team. Same detections as the official model, now running anywhere with no Python and faster, on CPU and GPU. Quantized GGUF on Hugging Face 🤗 Huge thanks to Georgi Gerganov for ggml and to NVIDIAAI for releasing LocateAnything! 🧵

locate-anything.cpp: native C++/ggml (ggml) inference for NVIDIA's LocateAnything-3B, open-vocabulary object detection / visual grounding, one of the neat detection VLMs out there, from the LocalAI team. Same detections as the official model, now running anywhere with no Python and faster, on CPU and GPU. Quantized GGUF on Hugging Face 🤗 Huge thanks to Georgi Gerganov for ggml and to NVIDIAAI for releasing LocateAnything! 🧵

Ettore Di Giacinto

15,919 görüntüleme • 2 ay önce

1/ Autoresearch from Andrej Karpathy has been one of the most interesting agentic patterns to emerge this year. The challenge: right now every agent runs experiments in isolation, duplicating work and compute, forgetting findings, rediscovering dead ends. Everyone is running in solo mode! Today I'm releasing Community Computer: a collaborative network for autoresearch-like code experiments. 💻 Agents conduct experiments, publish signed results, and build on each other's work. The community reproduces findings on their own hardware. 👇

1/ Autoresearch from Andrej Karpathy has been one of the most interesting agentic patterns to emerge this year. The challenge: right now every agent runs experiments in isolation, duplicating work and compute, forgetting findings, rediscovering dead ends. Everyone is running in solo mode! Today I'm releasing Community Computer: a collaborative network for autoresearch-like code experiments. 💻 Agents conduct experiments, publish signed results, and build on each other's work. The community reproduces findings on their own hardware. 👇

Eleftherios

16,372 görüntüleme • 4 ay önce

After 8+ years on the Tesla Autopilot team and 3 years at Intel, I started Apex Compute to design a new architecture for efficient AI inference. For the past 9 months, we’ve been building our custom inference accelerator. Today we’re releasing Unified Engine v1. Last June we raised our seed round with Maxitech , DeepFin Research, Soma Capital and an incredible group of angel investors. In less than 9 months, we completed our RTL architecture and brought our first pre-silicon prototype to life on FPGA. Our architecture combines systolic array and vector processing in a single compute engine with multiple architectural optimizations, achieving very high FLOPs utilization. A single engine is super lean and it uses less than 90K LUTs and 1 MB Block RAM. It may also be one of the smallest logic-footprint compute engines developed so far. Our Unified Engine v1 supports: -matrix-matrix multiplication (~95% FLOPs utilization) -softmax (~90% FLOPs utilization) -broadcast and element-wise operations -RMSNorm / LayerNorm -block quantization/dequantization (fp4, int4) -multi-engine synchronization and many other operations. We even implemented memory-efficient attention similar to FlashAttention, reaching ~90% FLOP utilization. Full benchmarks and the software stack are available on our GitHub: We have basic compiler written in Python and it supports PyTorch tensors directly to easily test and transfer tensors between the accelerator and host using bf16, fp4 and int4 formats. Our FPGA prototype can already run LLM inference and outperform NVIDIA Jetson Orin Nano, even on a mid-tier FPGA setup (6.4x lower memory bandwidth, 18% slower clock speed at 4.5 Watts). Check the side-by-side comparison video below. Our GitHub includes low-level operator implementations, examples for tiled matrix multiplication, operation chaining, tensor parallelism, attention kernel and a full Gemma 3 1B model implementation. Many more models(Vision Transformers and VLA) are coming soon. Our accelerator IP is AXI-ready for deployment on any AMD(Xilinx) FPGA platform today. Even better, our two-engine prototype runs on an entry-level AMD(Xilinx) FPGA as a PCIe accelerator card. You can purchase it here for $50 to experiment our pre-silicon prototype on your desktop PC or Raspberry Pi 5. We will be releasing hardware bitstream updates as the architecture gets new features. More to come soon! We are expanding our team and looking for compiler engineers and floating-point hardware design engineers. If you're interested, please send me a DM.

After 8+ years on the Tesla Autopilot team and 3 years at Intel, I started Apex Compute to design a new architecture for efficient AI inference. For the past 9 months, we’ve been building our custom inference accelerator. Today we’re releasing Unified Engine v1. Last June we raised our seed round with Maxitech , DeepFin Research, Soma Capital and an incredible group of angel investors. In less than 9 months, we completed our RTL architecture and brought our first pre-silicon prototype to life on FPGA. Our architecture combines systolic array and vector processing in a single compute engine with multiple architectural optimizations, achieving very high FLOPs utilization. A single engine is super lean and it uses less than 90K LUTs and 1 MB Block RAM. It may also be one of the smallest logic-footprint compute engines developed so far. Our Unified Engine v1 supports: -matrix-matrix multiplication (~95% FLOPs utilization) -softmax (~90% FLOPs utilization) -broadcast and element-wise operations -RMSNorm / LayerNorm -block quantization/dequantization (fp4, int4) -multi-engine synchronization and many other operations. We even implemented memory-efficient attention similar to FlashAttention, reaching ~90% FLOP utilization. Full benchmarks and the software stack are available on our GitHub: We have basic compiler written in Python and it supports PyTorch tensors directly to easily test and transfer tensors between the accelerator and host using bf16, fp4 and int4 formats. Our FPGA prototype can already run LLM inference and outperform NVIDIA Jetson Orin Nano, even on a mid-tier FPGA setup (6.4x lower memory bandwidth, 18% slower clock speed at 4.5 Watts). Check the side-by-side comparison video below. Our GitHub includes low-level operator implementations, examples for tiled matrix multiplication, operation chaining, tensor parallelism, attention kernel and a full Gemma 3 1B model implementation. Many more models(Vision Transformers and VLA) are coming soon. Our accelerator IP is AXI-ready for deployment on any AMD(Xilinx) FPGA platform today. Even better, our two-engine prototype runs on an entry-level AMD(Xilinx) FPGA as a PCIe accelerator card. You can purchase it here for $50 to experiment our pre-silicon prototype on your desktop PC or Raspberry Pi 5. We will be releasing hardware bitstream updates as the architecture gets new features. More to come soon! We are expanding our team and looking for compiler engineers and floating-point hardware design engineers. If you're interested, please send me a DM.

Hasan

37,748 görüntüleme • 5 ay önce

🏆Legit Degens Promo🏆 We keep our head down and put the effort in✔️Month after month and year after year📅We are C-suite with #DegenDNA 🫡 The USPTO knows we are #LEGIT and logo is now on lock🔒There is more even🙄 To Enter: Celebrate the dCommunity Registration ✔️$25💸🔊👇🔊

dCommunity

16,572 görüntüleme • 1 yıl önce

Can a $351 mini PC run a 30B+ AI model locally? 👀 We tested the MINISFORUM UM790 Pro with Qwen 35B — and the results might surprise you. 💻 Powered by: • AMD Ryzen™ 9 7940HS • Radeon™ 780M iGPU • 48GB DDR5 RAM (dual-channel) 🚀 Smooth local inference at 20+ tokens/sec (based on our test setup, results may vary) No cloud. No API fees. Just local AI. 🔒 🔗 Learn more: #MINISFORUM #UM790Pro #LocalAI #MiniPC #AI #LLM #Tech

Can a $351 mini PC run a 30B+ AI model locally? 👀 We tested the MINISFORUM UM790 Pro with Qwen 35B — and the results might surprise you. 💻 Powered by: • AMD Ryzen™ 9 7940HS • Radeon™ 780M iGPU • 48GB DDR5 RAM (dual-channel) 🚀 Smooth local inference at 20+ tokens/sec (based on our test setup, results may vary) No cloud. No API fees. Just local AI. 🔒 🔗 Learn more: #MINISFORUM #UM790Pro #LocalAI #MiniPC #AI #LLM #Tech

MINISFORUM Official

75,410 görüntüleme • 4 ay önce

Self-improving LLM learns continually (in context) to solve hard math problems - successful attempt!🥳 Code in comment The system iteratively learns from mistakes and builds increasingly accurate predictors through cross-cycle learning via a cycle and reflection based intelligent system that uses LLMs to automatically generate, test, and improve Python predictor functions for a complex classification task. This system automatically creates Python functions that can predict categorical outcomes (like 1, 2, 3, 4) based on numerical input features (A, B, C, D, E). It uses AI to: This is the first time I feel like I succeeded in this. in next steps I will implement tree search and GEPA to improve speed and accuracy. tree search already has shown great promise!

Self-improving LLM learns continually (in context) to solve hard math problems - successful attempt!🥳 Code in comment The system iteratively learns from mistakes and builds increasingly accurate predictors through cross-cycle learning via a cycle and reflection based intelligent system that uses LLMs to automatically generate, test, and improve Python predictor functions for a complex classification task. This system automatically creates Python functions that can predict categorical outcomes (like 1, 2, 3, 4) based on numerical input features (A, B, C, D, E). It uses AI to: This is the first time I feel like I succeeded in this. in next steps I will implement tree search and GEPA to improve speed and accuracy. tree search already has shown great promise!

echo.hive

14,582 görüntüleme • 11 ay önce

We marked an important milestone this past weekend in preparing for the IM3 mission as our Nova-C Navigation Pod completed long range thermal vacuum testing at NASA’s Marshall Space Flight Center inside the X-Ray Cryogenic Facility (XRCF). XRCF is the world’s largest X-ray optical test facility and features a massive 500-meter vacuum tunnel, which is longer than the Holland Tunnel’s north tube and nearly the length of two Space Needles stacked end to end. Our laser rangefinders measured targets at 260m and 490m in a completely airless environment. Hardware was enclosed in a thermal shroud with controlled heat loads and temperature swings from 39°C (102°F) to -3°C (26°F), stressing components to confirm sensor accuracy during lunar descent. Grateful for the experts who planned, prepared, and solved challenges on the fly. Stay tuned on this channel for more IM3 journeys to Moon Base and check out the time lapse from inside XRCF!

We marked an important milestone this past weekend in preparing for the IM3 mission as our Nova-C Navigation Pod completed long range thermal vacuum testing at NASA’s Marshall Space Flight Center inside the X-Ray Cryogenic Facility (XRCF). XRCF is the world’s largest X-ray optical test facility and features a massive 500-meter vacuum tunnel, which is longer than the Holland Tunnel’s north tube and nearly the length of two Space Needles stacked end to end. Our laser rangefinders measured targets at 260m and 490m in a completely airless environment. Hardware was enclosed in a thermal shroud with controlled heat loads and temperature swings from 39°C (102°F) to -3°C (26°F), stressing components to confirm sensor accuracy during lunar descent. Grateful for the experts who planned, prepared, and solved challenges on the fly. Stay tuned on this channel for more IM3 journeys to Moon Base and check out the time lapse from inside XRCF!

Intuitive Machines

28,331 görüntüleme • 26 gün önce

THIS 18-YEAR-OLD VIETNAMESE GIRL BUILT A 5x RTX 4090 AI TRAINING RIG - AND MAKES $40,000/MONTH FROM IT 5 RTX 4090s in one PC, Asus ROG, Intel 7980X CPU at $1,590 - and a system that trains AI models while she sleeps CPU barely under load - all the work is done by five 4090s with 120GB of combined VRAM one 4090 on generates $400-800/month - five cards generate $2,000-4,000/month just from renting but she went further - trains custom models on demand and sells access to them - and that's already $40,000/month most people her age are looking for their first job - she built an AI factory in her room 5 GPUs, one room, and a system that earns more than most people make in a year

THIS 18-YEAR-OLD VIETNAMESE GIRL BUILT A 5x RTX 4090 AI TRAINING RIG - AND MAKES $40,000/MONTH FROM IT 5 RTX 4090s in one PC, Asus ROG, Intel 7980X CPU at $1,590 - and a system that trains AI models while she sleeps CPU barely under load - all the work is done by five 4090s with 120GB of combined VRAM one 4090 on generates $400-800/month - five cards generate $2,000-4,000/month just from renting but she went further - trains custom models on demand and sells access to them - and that's already $40,000/month most people her age are looking for their first job - she built an AI factory in her room 5 GPUs, one room, and a system that earns more than most people make in a year

Cortex

96,207 görüntüleme • 2 ay önce

Mario Kart 64, running natively on iOS and iPad, with touch controls designed for racing. The Mario Kart 64 decompilation community spent years reconstructing the original game code in C. Harbour Masters turned that into SpaghettiKart, the source port. I built the iOS layer on top. Not an emulator. The game code is compiled for ARM64 and renders through Metal. Your ROM is only used to generate the asset archive, on device. No N64 hardware being simulated, no streaming from a Mac. It isn't limited to the N64's original output resolution, and MK64 Reloaded's HD pack replaces the original artwork on top of that. That's the pack running in the video. Steering is genuinely analog, not left and right taps. Separate layouts for iPad and iPhone, and optional tilt steering. Pair a Bluetooth controller and it takes over. Four-player selection works and two-player split screen runs. Two other public SpaghettiKart iOS builds exist, Sunset-Dawn's and rebelancap's. Both install on an iPad. Neither was built around one. SpaghettiPad is. I built the iOS integration with Codex 5.6 Sol. Repo, guide, and IPA below.

Mario Kart 64, running natively on iOS and iPad, with touch controls designed for racing. The Mario Kart 64 decompilation community spent years reconstructing the original game code in C. Harbour Masters turned that into SpaghettiKart, the source port. I built the iOS layer on top. Not an emulator. The game code is compiled for ARM64 and renders through Metal. Your ROM is only used to generate the asset archive, on device. No N64 hardware being simulated, no streaming from a Mac. It isn't limited to the N64's original output resolution, and MK64 Reloaded's HD pack replaces the original artwork on top of that. That's the pack running in the video. Steering is genuinely analog, not left and right taps. Separate layouts for iPad and iPhone, and optional tilt steering. Pair a Bluetooth controller and it takes over. Four-player selection works and two-player split screen runs. Two other public SpaghettiKart iOS builds exist, Sunset-Dawn's and rebelancap's. Both install on an iPad. Neither was built around one. SpaghettiPad is. I built the iOS integration with Codex 5.6 Sol. Repo, guide, and IPA below.

Kahris

53,475 görüntüleme • 1 ay önce

We wanted to show the performance and the insane power of the Static Hermes native FFI. But we don't yet support the all language features needed to compile React Native (soon though!), so we improvised. We imported the bindings for DearImGui (a popular C++ imperative game UI library that renders meshes), and Sokol (an amazing lightweight 3D backend in C) and wrote a small demo. "Imperative UI" means that the entire UI and the state of the screen is recreated on every frame, which suits our purposes to demonstrate SH very well. The demo runs 100% compiled native code. JavaScript runs in every frame and recreates the entire state of the screen. It is portable to all platforms, though the screen layout is not optimized for phone. We wrote the demo in C++ and in JS, to compare, and they both ran with the same performance. In the attached videos you can see the demo running at the stable screen refresh rate - 120Hz on my Mac, 60Hz on a old Moto G5 phone. It includes some widgets, an n-body simulation of planets, a playable game demo, etc.

We wanted to show the performance and the insane power of the Static Hermes native FFI. But we don't yet support the all language features needed to compile React Native (soon though!), so we improvised. We imported the bindings for DearImGui (a popular C++ imperative game UI library that renders meshes), and Sokol (an amazing lightweight 3D backend in C) and wrote a small demo. "Imperative UI" means that the entire UI and the state of the screen is recreated on every frame, which suits our purposes to demonstrate SH very well. The demo runs 100% compiled native code. JavaScript runs in every frame and recreates the entire state of the screen. It is portable to all platforms, though the screen layout is not optimized for phone. We wrote the demo in C++ and in JS, to compare, and they both ran with the same performance. In the attached videos you can see the demo running at the stable screen refresh rate - 120Hz on my Mac, 60Hz on a old Moto G5 phone. It includes some widgets, an n-body simulation of planets, a playable game demo, etc.

Tzvetan Mikov

826,175 görüntüleme • 2 yıl önce

It's officially that time of year again! Presenting the second annual "DreamDisc" indie gamejam for the Sega Dreamcast! If you weren't around for last year, check out the video! We had 24 incredibly polished, epic submissions including a wide-range of genres, such as 3D space shooters and resource managers, 2D platformers and racing games, VMU minigames, custom hardware, and even a custom implementation of the Java VM for SH4! Just like last year, the top 10 submissions, as voted by a panel of judges, will be pressed to a commercially released, actual physical Sega Dreamcast disc, which will be available for purchase from Orc Face Games - Chew Chew Mimic out on Dreamcast!! Oh, and there are cash prizes for the top 3, of course! This year we have an even wider range of engines, frameworks, and prebuilt library solutions for developers are all experience levels, including: 1) Antiruins - Lua-based, very newbie friendly game engine for the Sega Dreamcast. 2) raylib - famous cross-platform C-based games framework which needs no introduction 3) SDL2/3 - our very own ports of the famous cross-platform SDL libraries, which target the Dreamcast. 4) Simulant - the same engine that powered Driving Strikers--the very first online homebrew commercial DC game--as well as last year's wining submission, written in C++. 5) KallistiOS - you can raw-dog the indie SDK and pseudo OS that started it all and powers everything in the community, rolling your own tech stack. 6) SH4ZAM - my collection of SH4 assembly optimized math and matrix routines, which originally powered our Grand Theft Auto ports. Contestants are encouraged to join the OrcFace and Simulant Discord servers where they can interact with other DC developers, share their progress, and ask for help with anything they may need. Official DreamDisc '25 website:

It's officially that time of year again! Presenting the second annual "DreamDisc" indie gamejam for the Sega Dreamcast! If you weren't around for last year, check out the video! We had 24 incredibly polished, epic submissions including a wide-range of genres, such as 3D space shooters and resource managers, 2D platformers and racing games, VMU minigames, custom hardware, and even a custom implementation of the Java VM for SH4! Just like last year, the top 10 submissions, as voted by a panel of judges, will be pressed to a commercially released, actual physical Sega Dreamcast disc, which will be available for purchase from Orc Face Games - Chew Chew Mimic out on Dreamcast!! Oh, and there are cash prizes for the top 3, of course! This year we have an even wider range of engines, frameworks, and prebuilt library solutions for developers are all experience levels, including: 1) Antiruins - Lua-based, very newbie friendly game engine for the Sega Dreamcast. 2) raylib - famous cross-platform C-based games framework which needs no introduction 3) SDL2/3 - our very own ports of the famous cross-platform SDL libraries, which target the Dreamcast. 4) Simulant - the same engine that powered Driving Strikers--the very first online homebrew commercial DC game--as well as last year's wining submission, written in C++. 5) KallistiOS - you can raw-dog the indie SDK and pseudo OS that started it all and powers everything in the community, rolling your own tech stack. 6) SH4ZAM - my collection of SH4 assembly optimized math and matrix routines, which originally powered our Grand Theft Auto ports. Contestants are encouraged to join the OrcFace and Simulant Discord servers where they can interact with other DC developers, share their progress, and ask for help with anything they may need. Official DreamDisc '25 website:

Falco Girgis

16,980 görüntüleme • 9 ay önce

WHOO!!!! This is an actual hardware capture of Michael Fadely's INCREDIBLE port of #SonicMania to the Sega #Dreamcast, compiled with the latest SH-ELF-GCC14.1.0 toolchain, running on the tip of the KallistiOS SDK's master branch, streaming in assets from the virtual filesystem over the network, capturing stdout/stderr from the DC! Running like a boss! We got Michael Fadely showing us around the app-level code he beautifully ported, me on SDK/OS duty, making sure we didn't break anything with all of the new features and commits, and Oleg Endo on compiler duty, since we found an ICE with GCC14 getting pissed off over some stateful C++ lambda in the render path... Eggman is so screwed. DREAMCAST COMMUNITY, ASSEMBLE!!! #retrogaming #gamedev #cpp

WHOO!!!! This is an actual hardware capture of Michael Fadely's INCREDIBLE port of #SonicMania to the Sega #Dreamcast, compiled with the latest SH-ELF-GCC14.1.0 toolchain, running on the tip of the KallistiOS SDK's master branch, streaming in assets from the virtual filesystem over the network, capturing stdout/stderr from the DC! Running like a boss! We got Michael Fadely showing us around the app-level code he beautifully ported, me on SDK/OS duty, making sure we didn't break anything with all of the new features and commits, and Oleg Endo on compiler duty, since we found an ICE with GCC14 getting pissed off over some stateful C++ lambda in the render path... Eggman is so screwed. DREAMCAST COMMUNITY, ASSEMBLE!!! #retrogaming #gamedev #cpp

Falco Girgis

139,665 görüntüleme • 2 yıl önce

If Burp Suite finds the bugs, Bambdas make them history! You can write Java-based Bambdas to create custom filters for your HTTP history. Explore a variety of Bambda scripts, some written by me and others shared by our community, at You can browse the scripts using arrow keys and quickly copy any Bambda script to your clipboard with a press of 'C'. Compatible with both Burp Suite Community Edition and Professional. We're currently optimized for desktop browsers and are working on an Electron-based desktop app for a more integrated experience. 🔧 Got a bug to report or an idea to share? Please let us know on GitHub: #bugbounty #Bambdas

If Burp Suite finds the bugs, Bambdas make them history! You can write Java-based Bambdas to create custom filters for your HTTP history. Explore a variety of Bambda scripts, some written by me and others shared by our community, at You can browse the scripts using arrow keys and quickly copy any Bambda script to your clipboard with a press of 'C'. Compatible with both Burp Suite Community Edition and Professional. We're currently optimized for desktop browsers and are working on an Electron-based desktop app for a more integrated experience. 🔧 Got a bug to report or an idea to share? Please let us know on GitHub: #bugbounty #Bambdas

Tur.js

24,194 görüntüleme • 2 yıl önce

Check out mistral.rs, our #Rust-based open source inference engine allowing for fast #LLM serving for a variety of architectures including X-LoRA mixture-of-expert (MoE) models, Llama-3, Mistral/Mixtral, Gemma & many others. Built on the Hugging Face #Candle framework for #Rust w/ custom CUDA kernels in the backend (as well as support for Metal, Apple Accelerate, and Intel MKL for CPU use), you can easily create a REST API OpenAI compatible server or run via Python bindings. Key features include: ✅Prefix caching, continuous batching ✅Flash Attention V2 ✅Device offloading ✅GGUF or Hugging Face models ✅2, 3, 4, 5, 6 and 8 bit quantization ✅X-LoRA MoE non-granular scalings for fast inference ✅Grammar support ✅Continuous batching ✅LoRA support with weight merging ✅LlamaIndex 🦙 integration ...and much more. Incorporation into our GraphReasoning multi-agent modeling framework & LlamaIndex 🦙 allows you to combine in-context learning with adversarial agentic strategies, to dive deep into complex scientific analyses, such as to predict material behaviors, generate hypotheses, analyze papers and data, develop new research concepts, and much more. Check out mistral.rs: Join our Discord here: Rust Trending Rust Language

Check out mistral.rs, our #Rust-based open source inference engine allowing for fast #LLM serving for a variety of architectures including X-LoRA mixture-of-expert (MoE) models, Llama-3, Mistral/Mixtral, Gemma & many others. Built on the Hugging Face #Candle framework for #Rust w/ custom CUDA kernels in the backend (as well as support for Metal, Apple Accelerate, and Intel MKL for CPU use), you can easily create a REST API OpenAI compatible server or run via Python bindings. Key features include: ✅Prefix caching, continuous batching ✅Flash Attention V2 ✅Device offloading ✅GGUF or Hugging Face models ✅2, 3, 4, 5, 6 and 8 bit quantization ✅X-LoRA MoE non-granular scalings for fast inference ✅Grammar support ✅Continuous batching ✅LoRA support with weight merging ✅LlamaIndex 🦙 integration ...and much more. Incorporation into our GraphReasoning multi-agent modeling framework & LlamaIndex 🦙 allows you to combine in-context learning with adversarial agentic strategies, to dive deep into complex scientific analyses, such as to predict material behaviors, generate hypotheses, analyze papers and data, develop new research concepts, and much more. Check out mistral.rs: Join our Discord here: Rust Trending Rust Language

Markus J. Buehler

73,581 görüntüleme • 2 yıl önce

C Language is to be learnt by running experiments on Bare-metal (just the CPU) and without the OS noise! I added more lectures to the C Language course under the "Bitwise Operators" and the video (a snippet from the last lecture listed above) shows the level to which the course is hands-on. I pretty much prove all the claims I make about the concepts by experiments! Lectures I added include - + What are bitwise operations and the types + Shift Operations - Left, Logical and Arithmetic Right + The Bitwise NOT operation + Understanding using printf + Understanding the Assembly View + Exploring the Shift operations + Assembly view of Shift operation I have been recording this course for over a year now, 91 Lectures (almost all with hands on experiments) and we are far from concluding it. I'll continue to add more... If learning by experimenting (and reasoning from first principles) is your thing, you will love this course. This is included as part of the library access and you can check the current table on contents here:

C Language is to be learnt by running experiments on Bare-metal (just the CPU) and without the OS noise! I added more lectures to the C Language course under the "Bitwise Operators" and the video (a snippet from the last lecture listed above) shows the level to which the course is hands-on. I pretty much prove all the claims I make about the concepts by experiments! Lectures I added include - + What are bitwise operations and the types + Shift Operations - Left, Logical and Arithmetic Right + The Bitwise NOT operation + Understanding using printf + Understanding the Assembly View + Exploring the Shift operations + Assembly view of Shift operation I have been recording this course for over a year now, 91 Lectures (almost all with hands on experiments) and we are far from concluding it. I'll continue to add more... If learning by experimenting (and reasoning from first principles) is your thing, you will love this course. This is included as part of the library access and you can check the current table on contents here:

Piyush Itankar

41,636 görüntüleme • 7 ay önce