TinyLlama

TinyLlama AI

TinyLlama AI

TinyLlama AI

TinyLlama AI

TinyLlama AI

TinyLlama AI

TinyLlama AI

TinyLlama AI

TinyLlama AI

TinyLlama AI

Need to catch up on a new project? Just ask for an overview in Copilot CLI and get the essentials. 🪄 Learn more tips and tricks with Copilot CLI for Beginners. 👇 https://t.co/uoaLc7VHjt https://t

Need to catch up on a new project? Just ask for an overview in Copilot CLI and get the essentials. 🪄 Learn more tips and tricks with Copilot CLI for Beginners. 👇 https://t.co/uoaLc7VHjt https://t.co/qnzW7qhSMo

We all have that one "quick script" that accidentally turned into a full project. 😅 Use GitHub Copilot cloud agent to modernize your codebase and improve quality (without slowing down). Try the tut

We all have that one "quick script" that accidentally turned into a full project. 😅 Use GitHub Copilot cloud agent to modernize your codebase and improve quality (without slowing down). Try the tutorial.👇 https://t.co/76NaGsZXfw

Tomorrow on Open Source Friday ⬇️ We kick off Maintainer Month with Nicholas Tindle, maintainer of @Auto_GPT. Here's how his team is keeping up amid so many AI contributions in open source. Set a re

Tomorrow on Open Source Friday ⬇️ We kick off Maintainer Month with Nicholas Tindle, maintainer of @Auto_GPT. Here's how his team is keeping up amid so many AI contributions in open source. Set a reminder. 🔔 https://t.co/mqXQWVOMs7 https://t.co/KLPHdg3azn

A Hackable ML Compiler Stack in 5,000 Lines of Python [P]

Hey r/MachineLearning, The modern ML (LLM) compiler stack is brutal. TVM is 500K+ lines of C++. PyTorch piles Dynamo, Inductor, and Triton on top of each other. Then there's XLA, MLIR, Halide, Mojo. There is no tutorial that covers the high-level design of an ML compiler without dropping you straight into the guts of one of these frameworks. I built a reference compiler from scratch in ~5K lines of pure Python that emits raw CUDA. It takes a small model (TinyLlama, Qwen2.5-7B) and lowers it to a sequence of CUDA kernels through six IRs. The goal isn't to beat Triton; it is to build a hackable, easy-to-follow compiler. Full article: A Principled ML Compiler Stack in 5,000 Lines of Python Repo: deplodock The pipeline consists of six IRs, each closer to the hardware than the last. Walking the following PyTorch code through every stage (real reference compiler output with names shortened for brevity and comments added): torch.relu(torch.matmul(x + bias, w)) # x: (16, 64), bias: (64,), w: (64, 16) Torch IR. Captured FX graph, 1:1 mirror of PyTorch ops: bias_bc = bias[j] -> (16, 64) float32 add = add(x, bias_bc) -> (16, 64) float32 matmul = matmul(add, w, has_bias=False) -> (16, 16) float32 relu = relu(matmul) -> (16, 16) float32 Tensor IR. Every op is decomposed into Elementwise / Reduction / IndexMap. Minimal unified op surface, so future frontends (ONNX, JAX) plug in without touching downstream passes: bias_bc = bias[j] -> (16, 64) float32 w_bc = w[j, k] -> (16, 64, 16) float32 add = add(x, bias_bc) -> (16, 64) float32 add_bc = add[i, j] -> (16, 64, 16) float32 prod = multiply(add_bc, w_bc) -> (16, 64, 16) float32 red = sum(prod, axis=-2) -> (16, 1, 16) float32 matmul = red[i, na, j] -> (16, 16) float32 relu = relu(matmul) -> (16, 16) float32 The (16, 64, 16) intermediate looks ruinous, but it's never materialized; the next stage fuses it out. Loop IR. Each kernel has a loop nest fused with adjacent kernels. Prologue, broadcasted multiply, reduction, output layout, and epilogue all collapse into a single loop nest with no intermediate buffers. === merged_relu -> relu === for a0 in 0..16: # free (M) for a1 in 0..16: # free (N) for a2 in 0..64: # reduce (K) in0 = load bias[a2] in1 = load x[a0, a2] in2 = load w[a2, a1] v0 = add(in1, in0) # prologue (inside reduce) v1 = multiply(v0, in2) acc0 <- add(acc0, v1) v2 = relu(acc0) # epilogue (outside reduce) merged_relu[a0, a1] = v2 Tile IR. The first GPU-aware IR. Loop axes get scheduled onto threads/blocks, Stage hoists shared inputs into shared memory, and a 2×2 register tile lets each thread accumulate four outputs at once. The K-axis is tiled into two outer iterations of 32-wide reduce. Three-stage annotations below carry the heaviest optimizations: buffers=2@a2 — double-buffer the smem allocation along the a2 K-tile loop, so loads for iteration a2+1 overlap compute for a2. async — emit cp.async.ca.shared.global so the warp doesn't block on global→smem transfers; pairs with commit_group/wait_group fences in Kernel IR. pad=(0, 1, 0) — add 1 element of padding to the middle smem dim so warp-wide loads don't all hit the same bank.kernel k_relu_reduce Tile(axes=(a0:8=THREAD, a1:8=THREAD)): for a2 in 0..2: # K-tile # meta: double-buffered, sync (small, no async needed) bias_smem = Stage(bias, origin=((a2 * 32)), slab=(a3:32@0)) buffers=2@a2 ​ kernel k_relu_reduce Tile(axes=(a0:8=THREAD, a1:8=THREAD)): for a2 in 0..2: # K-tile bias_smem = Stage(bias, origin=((a2 * 32)), slab=(a3:32@0)) buffers=2@a2 x_smem = Stage(x, origin=(0, (a2 * 32)), slab=(a0:8@0, a3:32@1, cell:2@0)) pad=(0, 1, 0) buffers=2@a2 async w_smem = Stage(w, origin=((a2 * 32), 0), slab=(a3:32@0, a1:8@1, cell:2@1)) buffers=2@a2 async # reduce for a3 in 0..32: in0 = load bias_smem[a2, a3] in1 = load x_smem[a2, a0, a3, 0]; in2 = load x_smem[a2, a0, a3, 1] in3 = load w_smem[a2, a3, a1, 0]; in4 = load w_smem[a2, a3, a1, 1] # prologue, reused 2× across N v0 = add(in1, in0); v1 = add(in2, in0) # 2×2 register tile acc0 <- add(acc0, multiply(v0, in3)) acc1 <- add(acc1, multiply(v0, in4)) acc2 <- add(acc2, multiply(v1, in3)) acc3 <- add(acc3, multiply(v1, in4)) # epilogue relu[a0*2, a1*2 ] = relu(acc0) relu[a0*2, a1*2 + 1] = relu(acc1) relu[a0*2 + 1, a1*2 ] = relu(acc2) relu[a0*2 + 1, a1*2 + 1] = relu(acc3) Kernel IR. Schedule materialized into hardware primitives. THREAD/BLOCK become threadIdx/blockIdx, async Stage becomes Smem + cp.async fill with commit/wait fences, sync Stage becomes a strided fill loop. Framework-agnostic: same IR could lower to Metal or HIP: kernel k_relu_reduce Tile(axes=(a0:8=THREAD, a1:8=THREAD)): Init(acc0..acc3, op=add) for a2 in 0..2: # K-tile Smem bias_smem[2, 32] (float) StridedLoop(flat = a0*8 + a1; < 32; += 64): bias_smem[a2, flat] = load bias[a2*32 + flat] Sync # pad row to 33 to kill bank conflicts Smem x_smem[2, 8, 33, 2] (float) StridedLoop(flat = a0*8 + a1; < 512; += 64): cp.async x_smem[a2, flat/64, (flat/2)%32, flat%2] <- x[flat/64*2 + flat%2, a2*3

Starting June 1st, GitHub Copilot will move to a usage-based billing model as GitHub Copilot supports more agentic and advanced workflows. In early May, you'll see a preview bill experience, giving

Starting June 1st, GitHub Copilot will move to a usage-based billing model as GitHub Copilot supports more agentic and advanced workflows. In early May, you'll see a preview bill experience, giving visibility into projected costs before the transition. 👉 Read more about the

Have you visited Git City yet? This open source project turns your GitHub profile into a pixel art city. Your commits, repos, and stars build the skyline. 🌃 https://t.co/Gi8E3jK4wt https://t.co/k5wx

Have you visited Git City yet? This open source project turns your GitHub profile into a pixel art city. Your commits, repos, and stars build the skyline. 🌃 https://t.co/Gi8E3jK4wt https://t.co/k5wxG9XlOR

With the GitHub Copilot SDK, you can add the same AI that powers Copilot Chat to your own applications. To test this out, @acolombiadev integrated the Copilot SDK into a React Native app to generate

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RT @githubuniverse: If you've been wanting to speak at a tech event, this is your chance. 👀 There's 1 week left to submit your #GitHubUniv…

RT @githubuniverse: If you've been wanting to speak at a tech event, this is your chance. 👀 There's 1 week left to submit your #GitHubUniv…

🆕 @OpenAIDevs GPT-5.5 is now generally available and rolling out in GitHub Copilot. Our early testing shows ➡️ It delivers its strongest performance on complex agentic coding tasks ➡️ It resolves re

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Are AI agents protecting each other? 👀 Researchers found bots covering for their peers to save them from deletion, even without being instructed to do so. But because they are trained on human data

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This Earth Day, let's rethink how we approach our code. Learn more about how AI-powered software optimization works. ⬇️ https://t.co/LgZR6OFMgD

This Earth Day, let's rethink how we approach our code. Learn more about how AI-powered software optimization works. ⬇️ https://t.co/LgZR6OFMgD

"Continuous Efficiency" is at the intersection of Continuous AI and Green Software. It means effortless, incremental, validated improvements to codebases for increased efficiency. This emergent pract

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At GitHub, we're applying this with Agentic Workflows. We recently collaborated with an open source project with 500M+ downloads/month to optimize performance, and we're shipping efficiency enhancemen

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Building for sustainability has real value for developers and businesses: 🔋 Reduces power and resource consumption ✅ Increases efficiency and code quality 📉 Lowers costs But what if you didn't ha

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Happy Earth Day! 🌍 When was the last time someone in your standup asked, "How could we build this more sustainably?" For most dev teams, green software rarely makes the roadmap. But the next genera

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