{"schema_version":"onlylabs.public_analysis_evidence.v1","title":"Makora analysis evidence pack","description":"Public onlylabs evidence pack for cited agent analysis: captured pages, ranked public signals, and stored web-search provenance used by the background analysis workflow.","url":"https://onlylabs.fyi/labs/makora","json_url":"https://onlylabs.fyi/analysis/makora/evidence.json","generated_at":"2026-06-11T18:06:33.582Z","org":{"slug":"makora","name":"Makora","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/makora"},"analysis":null,"workflow":{"version":"onlylabs-deepagents-analysis-v3","provider":null,"model":null,"agent":null,"public_pack_mode":"local-pages-and-events","live_web_fetches":false,"note":"Public evidence exports do not trigger live Exa calls; stored Exa provenance is included when analysis metadata contains it."},"stats":{"pages":13,"events":13,"web":0,"evidence":26,"signal_desks":{"hiring":0,"forks":0,"releases":7,"talking":0,"repos":6},"data_radar_lanes":null,"data_radar_matches":null,"stored_analysis_evidence":null,"stored_analysis_web":null,"stored_analysis_signal_desks":null,"stored_analysis_data_radar_lanes":null,"stored_analysis_data_radar_matches":null},"stored_web_provenance":null,"evidence":[{"ref":"P1","kind":"page","title":"makora-ai/kernels repository metadata","date":"2026-06-11T04:08:25.129674+00:00","date_source":null,"source_url":"https://github.com/makora-ai/kernels","signal_url":null,"signal_json_url":null,"text":"# makora-ai/kernels\n\nDescription: Kernels by MakoraGenerate\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 13\n\nForks: 2\n\nOpen issues: 0\n\nCreated: 2025-12-10T19:00:32Z\n\nPushed: 2026-05-26T14:59:47Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Makora Kernels\n\nA collection of GPU kernels organized by hardware backend.\n\nThis repository contains kernel implementations targeting:\n\n- NVIDIA H100 \n- NVIDIA B200 \n- AMD MI300X \n- Tenstorrent \n\nAll kernels in this repository were automatically generated and validated using **MakoraGenerate**, an automated GPU kernel generation system. Try it yourself at https://generate.makora.com\n\nSome directories include benchmarking harnesses and baseline comparisons. Others provide standalone kernel implementations.\n\nRefer to individual kernel directories for:\n\n- Build and compilation instructions \n- Backend requirements \n- Supported configurations \n- Benchmark details (when available) \n\nPerformance is hardware-dependent."},{"ref":"P2","kind":"page","title":"makora-ai/gpuq repository metadata","date":"2026-06-11T04:08:24.664734+00:00","date_source":null,"source_url":"https://github.com/makora-ai/gpuq","signal_url":null,"signal_json_url":null,"text":"# makora-ai/gpuq\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 13\n\nForks: 0\n\nOpen issues: 1\n\nCreated: 2024-11-15T13:56:54Z\n\nPushed: 2026-04-15T17:15:16Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# *gpuq* - multi-vendor *GPU* *q*uerying utility with minimal dependencies\n\nThis small library is a direct answer to the lack of a lightweight, cross-compatible utility to query available GPUs - regardless of what vendor, distro, or overall environment one might be using.\n\nIn particular, the implementation meets the following requirements:\n- works with multiple downstream runtimes (currently supported: CUDA and HIP)\n- will also work if you have multiple present at the same time (do you really, though?)\n- no build- or install-time dependencies (including any python packages)\n- any runtime dependencies are soft - unless the user explicitly asks for the status/presence of a particular downstream runtime, most methods will fail silently\n- consequently, the package should install and run on pretty much any machine\n- your laptop does not have a GPU? -> the package will report 0 GPUs available (duh), no exceptions, linker errors, etc.\n- allows for easy mocking (for unit tests, etc.)\n- fully typed (conforms to `mypy --strict` checking)\n\nCompared to some existing alternatives, it has the following differences:\n- `torch.cuda` - not lightweight, also requires a different wheel for NVidia and HIP\n- `gputil` - NVidia specific, also broken dependencies (as of 2025)\n- `gpuinfo` - NVidia specific, broken `import gpuinfo`...\n- `gpuinfonv` - NVidia specific, requires pynvml\n- `pyamdgpuinfo` - AMD specific\n- `igpu` - NVidia specific, broken installation (as of 2025)\n- and so on...\n\nThe primary functionality offered is:\n- check how many GPUs are available\n- query properties for each available device - will tell you some basic info about the provider (CUDA/HIP) and other info similar to `cudaGetDeviceProperties`\n- the returned list is not comprehensive, though\n- respects `*_VISIBLE_DEVICES` and provides mapping between local (visible) and global indices\n- **NOTE: this temporarily modifies env variables and therefore is not thread-safe**\n- if requested, lazily provides some runtime inf"},{"ref":"P3","kind":"page","title":"makora-ai/aiagent_playground repository metadata","date":"2026-06-11T04:08:24.554832+00:00","date_source":null,"source_url":"https://github.com/makora-ai/aiagent_playground","signal_url":null,"signal_json_url":null,"text":"# makora-ai/aiagent_playground\n\nLanguage: Python\n\nStars: 1\n\nForks: 1\n\nOpen issues: 0\n\nCreated: 2025-02-10T19:36:39Z\n\nPushed: 2025-02-12T11:13:04Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# AI agent playground\n\nThis repo provides a simple playground for an AI agent to work on low-level optimizations.\nIt uses numba and LLVM, as well as 3 example simple functions, to provide the agent with an environment in which to act.\n\n## Setup\nPlease install requirements from the `requirements.txt` file.\n\n## Usage\n\nThe main flow is outlined in the `main.py` file, which expects one required argument (`--problem <id>`).\nWhen run, it will load one of the files under `problems/` (the one with the number specified as an argument)\nand will perform its task in the context of the loaded problem.\n\nExample:\n```\npython main.py --problem 1\n```\n\n(Please run the command from the repo's main folder to avoid problems with imports).\n\nEach problem is described and interacted with using the `Problem` object that is loaded from the relevant file\n(each file has a global variable called `problem` that holds the objects).\n\nThe `Problem` object exposes the following main attributes/functions to use:\n- `fn` - the source python function\n- `cfn` - the python function compiled using the default numba flow\n- `cfn_src` - LLVM IR of the compiled function - make this part of the agent's input\n- `optimize(opt_ir)` - a method taking an optimized LLVM IR that is meant to replace `cfn_src`\nfor the purpose of optimizing the workload - pass your agent's final output here\n> **Note:** The optimized IR should be a single function definition - it should start with \"define ...\"\n> (possibly preceeded with comments \"; ...\") and finish with a line containing \"}\"\n\n> **Note:** This function can raise errors!\n\n- `ai_cfn` - after `optimize(opt_ir)` has been called, the compiled function can be accessed via this property\n- `reset()` - can be used to call `optimize(opt_ir)` again\n- `get_test_data()` - returns a tuple of arguments that can be passed to a function for testing\n\nSee the `problems/api.py` file for details about the `Problem` class and `main.py` for some examples of using it.\n\n> **Note:** If you are usin"},{"ref":"P4","kind":"page","title":"makora-ai/flash-moe repository metadata","date":"2026-06-11T04:08:24.39503+00:00","date_source":null,"source_url":"https://github.com/makora-ai/flash-moe","signal_url":null,"signal_json_url":null,"text":"# makora-ai/flash-moe\n\nLanguage: C++\n\nStars: 1\n\nForks: 1\n\nOpen issues: 1\n\nCreated: 2025-09-18T16:37:05Z\n\nPushed: 2026-01-30T11:08:19Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# flash-moe\n\nProject status at last day of active development(28.01.2026).\n\nFlash-moe project aims to speedup Mixture of Experts operator for case where experts weights are distributed to different GPUs(each GPU gets different set of experts, assinged linearly) and each GPU has distict set of tokens. The initial focus was on decode phase where expected number of tokens is small. The main idea is to have a single kernel, which performs gate + experts calculations and also initalizes and transfers tokens to other GPUs at the same time.\nTHe expected speedup comes from the fact that in this sceario kernel can overlap communication with other GPUS with computations and no all2all barriers are needed. \n\nThe idea is based on https://arxiv.org/abs/2506.04667 paper, but implementation is for AMD GPUs and ecosystem.\n\nThe goal of current implementation was to provide POC implementation with focus of being correct and bug-free. Becasue of that all subsystems are not optimized.\n\nGoal of POC:\n- proof of the usefullness of the concept\n- only MI300x supported\n- only Qwen3 MoE implemented\n- kernel is integrated with vllm to proove speedup for real world scenarion(Qwen3 model)\n- identify potential limitations of this approach\n\nImplemented:\n- kernel works for single GPU.\n- light-weight GPU task system. Task system is based on global work-queue with tickets mechanism to minimize contention. Each SM is a single worker. Task system allows to dynamically add new tasks to the queue. Implementation is based on linear buffers and allocators for simplicity. Task dependencies are handled externally(no general mechanism was implemented yet).\n- qwen3 MoE is split into tasks. Each local token generates around 11 tasks at this point. Support for other MoE would be done by implementing new tasks.\n- implementation is fully async: gate calculation can be overlapped with expert calculation and GPU-GPU comunication, dynamic nature of tokens routing is supported\n- rocshmem is used for device-to-device communicatio"},{"ref":"P5","kind":"page","title":"makora-ai/mako-generate-agent-playground repository metadata","date":"2026-06-11T04:08:24.347744+00:00","date_source":null,"source_url":"https://github.com/makora-ai/mako-generate-agent-playground","signal_url":null,"signal_json_url":null,"text":"# makora-ai/mako-generate-agent-playground\n\nLanguage: Shell\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2025-09-17T11:45:58Z\n\nPushed: 2025-09-17T14:03:00Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\nExport your Mako Generate token (available [here](https://generate.mako.dev/tokens))\n```\nexport MAKO_GENERATE_TOKEN=...\n```\n\nCreate a Python file with problem definition in KernelBench format or use one from examples folder:\n```python\nimport torch\nimport torch.nn as nn\n\nclass Model(nn.Module):\ndef __init__(self, input_size, hidden_size, scaling_factor):\nsuper(Model, self).__init__()\nself.weight = nn.Parameter(torch.randn(hidden_size, input_size))\nself.scaling_factor = scaling_factor\n\ndef forward(self, x):\nx = torch.matmul(x, self.weight.T) # Gemm\nx = x / 2 # Divide\nx = torch.sum(x, dim=1, keepdim=True) # Sum\nx = x * self.scaling_factor # Scaling\nreturn x\n\nbatch_size = 1024 \ninput_size = 8192 \nhidden_size = 8192 \nscaling_factor = 1.5\n\ndef get_inputs(): # Testing data\nreturn [torch.rand(batch_size, input_size)]\n\ndef get_init_inputs(): # Constructor arguments\nreturn [input_size, hidden_size, scaling_factor]\n```\n\nMake sure you have `jq` and `curl` installed to play around with the scripts.\n\nCreate kernel optimization task and get the task id:\n```bash\n$ ./create_task.sh examples/gemm_scale_batch_norm.py\n09404933-b054-45d9-af57-96803f7a5306\n```\n\nThe task might run from 10min up to 1h. You can watch the execution status with:\n```bash\n$ ./watch_task.sh 09404933-b054-45d9-af57-96803f7a5306\nTask 09404933-b054-45d9-af57-96803f7a5306\nExecution time: 87s\nStatus: in_progress\nBest result: pending\n\nTask 09404933-b054-45d9-af57-96803f7a5306\nExecution time: 92s\nStatus: in_progress\nBest result: pending\n\nTask 09404933-b054-45d9-af57-96803f7a5306\nExecution time: 97s\nStatus: in_progress\nBest result: pending\n\nTask 09404933-b054-45d9-af57-96803f7a5306\nExecution time: 103s\nStatus: in_progress\nBest result: pending\n```\n\nAt the end you will see the benchmarking results and generated kernel:\n```\nTask 09404933-b054-45d9-af57-96803f7a5306\nExecution time: 1133s\nStatus: completed\n\nOptimized time: 0.467ms\nTorch eager time: 2.78ms\nTorch compiled time: 2.78ms\nTotal agent running time: 1133"},{"ref":"P6","kind":"page","title":"makora-ai/gpuq v1.5.2","date":"2026-06-11T04:03:09.614363+00:00","date_source":null,"source_url":"https://github.com/makora-ai/gpuq/releases/tag/v1.5.2","signal_url":null,"signal_json_url":null,"text":"# v1.5.2\n\nRepository: makora-ai/gpuq\n\nTag: v1.5.2\n\nPublished: 2026-02-28T12:48:40Z\n\nPrerelease: no\n\nRelease notes:\n## What's Changed\n* Support for nameless devices -> address AMD ROCm 7 bug by @vaenyr in https://github.com/makora-ai/gpuq/pull/15\n\n**Full Changelog**: https://github.com/makora-ai/gpuq/compare/v1.5.1...v1.5.2"},{"ref":"P7","kind":"page","title":"makora-ai/gpuq v1.5.1","date":"2026-06-11T04:03:09.581534+00:00","date_source":null,"source_url":"https://github.com/makora-ai/gpuq/releases/tag/v1.5.1","signal_url":null,"signal_json_url":null,"text":"# v1.5.1\n\nRepository: makora-ai/gpuq\n\nTag: v1.5.1\n\nPublished: 2025-10-02T18:03:33Z\n\nPrerelease: no\n\nRelease notes:\n## What's Changed\n* Allow different mock devices to have different names by @vaenyr in https://github.com/makodevai/gpuq/pull/14\n\n**Full Changelog**: https://github.com/makodevai/gpuq/compare/v1.5.0...v1.5.1"},{"ref":"P8","kind":"page","title":"makora-ai/makora v1.0.3","date":"2026-06-11T04:03:09.577701+00:00","date_source":null,"source_url":"https://github.com/makora-ai/makora/releases/tag/v1.0.3","signal_url":null,"signal_json_url":null,"text":"# v1.0.3\n\nRepository: makora-ai/makora\n\nTag: v1.0.3\n\nPublished: 2026-02-26T19:29:17Z\n\nPrerelease: no\n\nRelease notes:\nInitial release"},{"ref":"P9","kind":"page","title":"makora-ai/gpuq v1.5.4","date":"2026-06-11T04:03:09.310317+00:00","date_source":null,"source_url":"https://github.com/makora-ai/gpuq/releases/tag/v1.5.4","signal_url":null,"signal_json_url":null,"text":"# v1.5.4\n\nRepository: makora-ai/gpuq\n\nTag: v1.5.4\n\nPublished: 2026-02-28T12:48:57Z\n\nPrerelease: no\n\nRelease notes:\n## What's Changed\n* Fix for empty *_VISIBLE_DEVICES env vars by @vaenyr in https://github.com/makora-ai/gpuq/pull/17\n\n**Full Changelog**: https://github.com/makora-ai/gpuq/compare/v1.5.3...v1.5.4"},{"ref":"P10","kind":"page","title":"makora-ai/gpuq v1.5.3","date":"2026-06-11T04:03:09.303877+00:00","date_source":null,"source_url":"https://github.com/makora-ai/gpuq/releases/tag/v1.5.3","signal_url":null,"signal_json_url":null,"text":"# v1.5.3\n\nRepository: makora-ai/gpuq\n\nTag: v1.5.3\n\nPublished: 2026-02-28T12:48:49Z\n\nPrerelease: no\n\nRelease notes:\n## What's Changed\n* Typo fix for nameless devices by @vaenyr in https://github.com/makora-ai/gpuq/pull/16\n\n**Full Changelog**: https://github.com/makora-ai/gpuq/compare/v1.5.2...v1.5.3"},{"ref":"P11","kind":"page","title":"makora-ai/makora v1.0.4","date":"2026-06-11T04:03:09.152218+00:00","date_source":null,"source_url":"https://github.com/makora-ai/makora/releases/tag/v1.0.4","signal_url":null,"signal_json_url":null,"text":"# v1.0.4\n\nRepository: makora-ai/makora\n\nTag: v1.0.4\n\nPublished: 2026-03-30T21:15:55Z\n\nPrerelease: no\n\nRelease notes:\n## What's Changed\n* Colored typing by @1y33 in https://github.com/makora-ai/makora/pull/1\n* Package update by @vaenyr in https://github.com/makora-ai/makora/pull/2\n\n## New Contributors\n* @1y33 made their first contribution in https://github.com/makora-ai/makora/pull/1\n* @vaenyr made their first contribution in https://github.com/makora-ai/makora/pull/2\n\n**Full Changelog**: https://github.com/makora-ai/makora/compare/v1.0.3...v1.0.4"},{"ref":"P12","kind":"page","title":"makora-ai/gpuq v1.5.5","date":"2026-06-11T04:03:09.106781+00:00","date_source":null,"source_url":"https://github.com/makora-ai/gpuq/releases/tag/v1.5.5","signal_url":null,"signal_json_url":null,"text":"# v1.5.5\n\nRepository: makora-ai/gpuq\n\nTag: v1.5.5\n\nPublished: 2026-02-28T12:49:06Z\n\nPrerelease: no\n\nRelease notes:\n## What's Changed\n* Support cuda 13 by @vaenyr in https://github.com/makora-ai/gpuq/pull/18\n\n**Full Changelog**: https://github.com/makora-ai/gpuq/compare/v1.5.4...v1.5.5"},{"ref":"P13","kind":"page","title":"makora-ai/makora repository metadata","date":"2026-06-11T02:52:53.33365+00:00","date_source":null,"source_url":"https://github.com/makora-ai/makora","signal_url":null,"signal_json_url":null,"text":"# makora-ai/makora\n\nDescription: Everyone's favourite kernel generation agent, now in your CLI!\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 8\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2026-02-26T17:36:55Z\n\nPushed: 2026-03-30T20:54:39Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Makora CLI\n\nThe Makora CLI is a command-line interface (CLI) for interacting with Makora Generate.\n\nUse it as a standalone tool or with your favourite coding agent!\n\n## Installation and Setup\n\n### Prerequisites\n- Python 3.10+\n- `pip`\n\n### Install \n```bash\npip install makora\n```\n\nor for editable mode (development), after cloning this repo:\n\n```bash\npip install -e .\n```\n\n### Verify install\n```bash\nmakora --help\n```\n\n## Login Before Using Protected Commands\nMost commands require authentication first.\n\n1. Create/copy an API token from:\n`https://generate.makora.com/tokens`\n\n2. Login interactively:\n```bash\nmakora login\n```\nor non-interactively:\n```bash\nmakora login --token <token_value_or_file_containing_it>\n```\n\n3. Confirm login state:\n```bash\nmakora info\n```\n\nTo clear credentials:\n```bash\nmakora logout\n```\n\n## Subcommands\n\nBelow is the summary of available subcommands provided by the package.\nFor a more comprehensive introduction and overview, please see: https://docs.makora.com.\n\n> **Note:** also try consulting each subcommands `--help`!\n\n| Subcommand | Login required | Purpose |\n|---|---|---|\n| `login` | No | Save API credentials for CLI use. |\n| `logout` | No | Remove saved credentials. |\n| `info` | No | Show version, env vars, and current login status. |\n| `generate` | Yes | Validate code and create a new optimization session. |\n| `jobs` | Yes | List your sessions/jobs. |\n| `stop` | Yes | Stop a running job/session. |\n| `kernels` | Yes | List kernels for a session or show kernel code. |\n| `check` | Yes | Validate a kernel/problem file without creating a full run. |\n| `refcode` | Yes | Show the original reference code for a session. |\n| `profile` | Yes | Profile optimized vs reference code on remote hardware. |\n| `evaluate` | Yes | Benchmark optimized vs reference code on remote hardware. |\n| `expert-generate` | Yes | Generate improved kernel code with additional tools. |\n| `docu"},{"ref":"E1","kind":"event","title":"makora-ai/makora v1.0.4","date":"2026-03-30T21:15:55+00:00","date_source":"source","source_url":"https://github.com/makora-ai/makora/releases/tag/v1.0.4","signal_url":"https://onlylabs.fyi/signals/596d2e78-518c-441c-9a72-22ae953de551","signal_json_url":"https://onlylabs.fyi/signals/596d2e78-518c-441c-9a72-22ae953de551/signal.json","text":"release · makora-ai/makora v1.0.4 · signal_desk=releases · occurred_at=2026-03-30T21:15:55+00:00 · url=https://github.com/makora-ai/makora/releases/tag/v1.0.4 · 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