{ "netuid": 56, "slug": "gradients", "name": "Gradients", "symbol": "\u062c", "description": "Best AutoML plaftorm in the world", "priceTao": 0.021681381, "priceUsd": 5.486497435512687, "athUsd": 5.486497435512687, "change24h": -1.0788426231781965, "change7d": 0.22124001185382738, "change30d": -1.886936149976358, "change90d": 0.0, "marketCapUsd": 24027203.212592002, "volume24hUsd": 134739.8337469303, "rootProp": 0.1599960983909825, "liquidityUsd": 29418925.598287635, "alphaStaked": 1696472.839013649, "activeMiners": 21, "activeValidators": 15, "emissionPct": 0.0010531858555388328, "emissionPerDayTao": 1.0, "registrationCost": 0.0, "pruningRank": 11, "immunityRemaining": 4849, "sentiment": "Neutral", "githubRepo": "https://github.com/gradients-ai/G.O.D", "subnetUrl": "https://www.gradients.io/", "scorecard": { "adoption": { "label": "weak", "score": 30, "reasons": [ { "tone": "neutral", "label": "Stars", "value": "34", "weight": null }, { "tone": "neutral", "label": "Contributors", "value": "7", "weight": null }, { "tone": "neutral", "label": "24h volume", "value": "$134,740", "weight": null }, { "tone": "neutral", "label": "Validators", "value": "15", "weight": null }, { "tone": "positive", "label": "Bus factor", "value": "Top contributor: 48%", "weight": null }, { "tone": "neutral", "label": "LLM blend", "value": "heuristic=25 \u00d7 0.6 + llm=38 \u00d7 0.4", "weight": null } ] }, "shipSpeed": { "label": "strong", "score": 76, "reasons": [ { "tone": "positive", "label": "Commits 30d", "value": "23", "weight": null }, { "tone": "positive", "label": "Merged PRs 30d", "value": "24", "weight": null }, { "tone": "neutral", "label": "Releases 12m", "value": "0", "weight": null }, { "tone": "positive", "label": "Latest push", "value": "2026-04-29T20:24:15Z", "weight": null }, { "tone": "neutral", "label": "Momentum", "value": "stable (23/30d vs 23/mo avg)", "weight": null }, { "tone": "neutral", "label": "LLM blend", "value": "heuristic=90 \u00d7 0.6 + llm=55 \u00d7 0.4", "weight": null } ] }, "updatedAt": "2026-04-29T20:41:08.734073+00:00", "confidence": 0.82, "usefulness": { "label": "watch", "score": 52, "reasons": [ { "tone": "neutral", "label": "Durable keywords", "value": "1 matched", "weight": null }, { "tone": "positive", "label": "Commodity risk", "value": "0 matched", "weight": null }, { "tone": "neutral", "label": "Subnet traction", "value": "6/30", "weight": null }, { "tone": "neutral", "label": "LLM blend", "value": "heuristic=46 \u00d7 0.6 + llm=62 \u00d7 0.4", "weight": null } ] }, "codeQuality": { "label": "weak", "score": 28, "reasons": [ { "tone": "negative", "label": "CI", "value": "Missing", "weight": null }, { "tone": "positive", "label": "Tests", "value": "1 dir(s), framework config", "weight": null }, { "tone": "positive", "label": "Docs", "value": "Basic (600 chars)", "weight": null }, { "tone": "negative", "label": "Dep mgmt", "value": "None", "weight": null }, { "tone": "negative", "label": "PR reviews", "value": "0.0 avg, 100% self-merge", "weight": null }, { "tone": "neutral", "label": "Commit quality", "value": "0% conventional", "weight": null }, { "tone": "neutral", "label": "Security hygiene", "value": "No SECURITY.md", "weight": null }, { "tone": "neutral", "label": "LLM blend", "value": "heuristic=26 \u00d7 0.6 + llm=30 \u00d7 0.4", "weight": null } ] } }, "marketScore": 28.0, "githubScore": 53.0, "aiScore": 48.0, "masterScore": 40.0, "masterRating": "C", "lastUpdatedAt": "2026-04-29T20:21:48Z", "isStale": false, "masterScorePrev": 40.0, "purpose": { "title": "Gradients", "source": "llm", "confidence": 0.72, "shortSummary": "Gradients (SN56) is a tournament-based AutoML subnet where miners submit open-source LLM and diffusion model training scripts that validators execute on dedicated infrastructure over 4-7 day competitive cycles. The concept is genuinely differentiated within the Bittensor ecosystem, but repository hygiene is poor \u2014 100% self-merge ratio, zero CI, zero PR reviews \u2014 undermining confidence in code correctness. With 21 active miners, 15 validators, and a $24M market cap, there is real on-chain activity, but the community footprint (34 stars, 7 contributors) is thin relative to the valuation." }, "analysis": { "risks": [ "Self-merge ratio of 1.0 (every single PR merged without review), combined with 0.0 commit message quality score and no CI or lint config, means there is zero code review culture and no automated correctness gate \u2014 a critical red flag for a training infrastructure project", "Community footprint is thin relative to $24M market cap: only 34 stars, 36 forks, 7 contributors, and zero formal releases in the past 12 months \u2014 indicating the project may be validator/team-driven with minimal external developer adoption or ecosystem trust" ], "score": 48, "summary": "Gradients (SN56) is a tournament-based AutoML subnet where miners submit open-source LLM and diffusion model training scripts that validators execute on dedicated infrastructure over 4-7 day competitive cycles. The concept is genuinely differentiated within the Bittensor ecosystem, but repository hygiene is poor \u2014 100% self-merge ratio, zero CI, zero PR reviews \u2014 undermining confidence in code correctness. With 21 active miners, 15 validators, and a $24M market cap, there is real on-chain activity, but the community footprint (34 stars, 7 contributors) is thin relative to the valuation.", "provider": "cascade:sonnet", "expiresAt": "2026-05-29T20:34:40.670605+00:00", "strengths": [ "Tournament model is architecturally differentiated: miners compete with open-source training scripts run on validator infrastructure over 4-7 day cycles \u2014 not a commodity LLM wrapper", "Consistent shipping cadence: 23 commits and 24 merged PRs in the last 30 days, with 69 commits over 90 days showing stable ~23 commits/month velocity", "Real liquidity and on-chain activity: $24M market cap, $29.4M liquidity, and $134K 24h volume with 21 active miners and 15 active validators confirm the subnet is live and in use" ], "updatedAt": "2026-04-29T20:34:40.670605+00:00", "durability": "watch", "recommendation": "Monitor for addition of CI, PR review culture, and formal releases before increasing conviction \u2014 the tournament concept has real merit but the engineering process signals are too weak to trust at this valuation.", "dimensionScores": { "adoption": 38, "shipSpeed": 55, "usefulness": 62, "codeQuality": 30 } }, "repoCoverageStatus": "covered", "image": "https://www.gradients.io/favicon.png", "repos": [ { "netuid": 56, "repoKind": "primary", "provider": "github", "owner": "gradients-ai", "name": "G.O.D", "url": "https://github.com/gradients-ai/G.O.D", "source": "registry", "isArchived": false, "isFork": false } ], "repoSnapshots": [ { "fullName": "gradients-ai/G.O.D", "description": "Gradients on demand", "homepageUrl": null, "defaultBranch": "main", "stars": 34, "forks": 36, "watchers": 1, "contributors": 7, "commits30d": 23, "commits90d": 69, "mergedPrs30d": 24, "mergedPrs90d": 74, "releases12m": 0, "openIssues": 31, "closedIssues30d": 0, "languages": [ { "name": "Python", "percentage": 97.8 }, { "name": "Shell", "percentage": 1.4 }, { "name": "Dockerfile", "percentage": 0.7 }, { "name": "PLpgSQL", "percentage": 0.1 } ], "topics": [], "rootSignals": { "hasCi": false, "hasTests": true, "hasLintConfig": false, "hasFormatterConfig": false, "hasTypeScript": false, "hasLicense": true, "hasSecurityPolicy": false, "hasDocs": true, "hasDocker": true, "hasDependabot": false, "hasRenovate": false, "rootEntries": [ ".dev", ".devcontainer", ".gitignore", ".pre-commit-config.yaml", ".vscode", "LICENSE.md", "NOTICE", "README.md", "SKILL.md", "Taskfile.yml", "auditing", "bootstrap.sh", "config", "core", "create_synthetic_env_task.py", "docker-compose.dev.yml", "docker-compose.observability-server.yml", "docker-compose.observability.yml", "docker-compose.trainer-server.yml", "docker-compose.yml", "dockerfiles", "docs", "examples", "miner", "pyproject.toml", "scripts", "tests", "trainer", "utils", "validator" ] }, "readmeExcerpt": "

G.O.D Subnet

\ud83d\ude80 Welcome to the Gradients on Demand https://gradients.io Subnet > Distributed intelligence for LLM and diffusion model training. Where the world's best AutoML minds compete. **Tournaments** \ud83c\udfc6 Competitive events where the validator executes miners' open-source training scripts on dedicated infrastructure. - **Duration**: 4-7 days per tournament - **Frequency**: New tournaments start 72 hours after the previous one ends - **Rewards**: Exponentially higher weight potential for top performers - **Open Source**: Winning AutoML scripts are released when tourname", "pushedAt": "2026-04-29T20:24:15Z", "isArchived": false, "isFork": false, "commitMessageQuality": 0.0, "selfMergeRatio": 1.0, "avgReviewsPerPr": 0.0, "hasBranchProtection": false, "topContributorPct": 0.48, "avgCommentsPerIssue": 0.05, "avgResponseHours": 0.0, "prMergeHoursP50": 0.0, "prMergeHoursP95": 6.0, "contributorChurn30d": 0.0, "vulnerabilityCount": 0 } ], "externalLinks": [ { "label": "Website", "href": "https://www.gradients.io/" }, { "label": "GitHub", "href": "https://github.com/gradients-ai/G.O.D" } ], "priceHistory": [ { "timestamp": 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"media_type": null }, { "tweet_id": "2040248458477813885", "handle": "gradients_ai", "text": "RT @proofoftalk: The best is yet to come.", "created_at": "2026-04-03T23:01:53-03:00", "likes": 45, "retweets": 4, "replies": 1, "views": 2932, "is_retweet": false, "is_reply": false, "media_type": null }, { "tweet_id": "2039824612318712152", "handle": "gradients_ai", "text": "We bought back and burned alpha using the 164 TAO collected as tournament fees since the last burn. Roughly worth $49000 at the current pricing.\n\nExtrinsic: https://t.co/zO8Ic1iJtP", "created_at": "2026-04-02T18:57:40-03:00", "likes": 89, "retweets": 8, "replies": 3, "views": 2925, "is_retweet": false, "is_reply": false, "media_type": null }, { "tweet_id": "2039778180006109379", "handle": "gradients_ai", "text": "We just had a new image tournament winner on Gradients! Pushing the boundaries of the diffusion training even further. Some of the changes that we figured helped them win:\n\n- LLaVA 1.5 based image captioning script to re-caption the images in the dataset.\n\n- Lots of hyperparameter tuning, including increased LR and decreased batch size for Qwen and Z-Image.\n\n- Optimized configs for each model, dataset size and style/person combination.\n\nCongrats to the new winner! Let's keep pushing \ud83e\udd19\n\nHow good are the models trained with the latest code? \nWell, we missed @const_reborn in San Francisco, so we trained yet another const model to make it happen:", "created_at": "2026-04-02T15:53:10-03:00", "likes": 71, "retweets": 3, "replies": 3, "views": 3216, "is_retweet": false, "is_reply": false, "media_type": null }, { "tweet_id": "2039248472185196673", "handle": "gradients_ai", "text": "It was such an awesome event in San Francisco!\nGreat speakers, product announcements, networking, and ideas. Lots of newcomers to the Bittensor system as well...\nBrilliant\n\nMany thanks to @SiliconJose and the @btlabs_ai team for hosting us", "created_at": "2026-04-01T04:48:17-03:00", "likes": 62, "retweets": 2, "replies": 3, "views": 5101, "is_retweet": false, "is_reply": false, "media_type": null }, { "tweet_id": "2031491225132609601", "handle": "gradients_ai", "text": "Open-source tournaments keep on delivering... \n\nThe miners are improving nicely on the new RL environment training tasks. The Gin Rummy environment from the Affinetes GAME was especially fruitful, as seen below.\n\nAfter Goospiel, Alfworld, and Gin Rummy we are expanding to other environments from Affinetes until we get the best scripts to train on all of them", "created_at": "2026-03-10T19:03:46-03:00", "likes": 69, "retweets": 9, "replies": 0, "views": 7826, "is_retweet": false, "is_reply": false, "media_type": null }, { "tweet_id": "2030720679155212687", "handle": "gradients_ai", "text": "RT @const_reborn: People beginning to realize that when software is cheap all that remains is digital commodities behind APIs, your agent n\u2026", "created_at": "2026-03-08T16:01:53-03:00", "likes": 647, "retweets": 109, "replies": 22, "views": 53797, "is_retweet": false, "is_reply": false, "media_type": null }, { "tweet_id": "2010772750978146474", "handle": "gradients_ai", "text": "New Sheriff in Text Town! \ud83e\udd19\n\nWe have a new winner in our open-source tournaments for text AutoML scripts.\n\nHow did they beat the old champ?\n\n1. Early Training (100 steps with default LR):\n - Measures training speed\n - Estimates how many runs fit in remaining time\n - Records loss and stops early\n\n2. LR Grid Search (5-9 runs with different LRs):\n - Generates LRs logarithmically around initial LR (0.66x to 1.51x)\n - Each run trains to step 100 only\n - Records loss, deletes poor checkpoints\n - Finds which LR gives best early loss\n\n3. Final Training (Full training with best LR):\n - Uses the winning LR from step 2\n - Trains to completion\n\nThere were other improvements in training time utilization, handling crashes, checkpoint selection and the general supporting infra using Redis etc.\n\nKudos (and emissions) to the new champ!\n\nFind the latest AutoML scripts under:\nhttps://t.co/tOxImvhAjX", "created_at": "2026-01-12T14:55:56-03:00", "likes": 36, "retweets": 7, "replies": 1, "views": 7114, "is_retweet": false, "is_reply": false, "media_type": null }, { "tweet_id": "2006768721315147829", "handle": "gradients_ai", "text": "\ud83d\udce2103 TAO worth of alpha buyback and burn:\nAs promised, the collected tournament fees have been used to buy alpha and then burn it. \ud83d\udd25\n\nThe extrinsic can be found on taostats: https://t.co/SMlu5iW7oB\n\nHappy New Year from the Gradients team! \ud83e\udd19", "created_at": "2026-01-01T13:45:21-03:00", "likes": 100, "retweets": 12, "replies": 2, "views": 3139, "is_retweet": false, "is_reply": false, "media_type": null }, { "tweet_id": "2003433859497554067", "handle": "gradients_ai", "text": "2/2\n\nOur YaRN-extended Covenant-Chat (32k context) demonstrates what's possible when you combine extended context windows with optimized gradient-based training. Longer context means the model sees more relevant information during each training step, leading to stronger learning signals and faster convergence.\n\nYaRN is already integrated into the platform and the tournaments - the full post-training pipeline coming to users in January \ud83e\udd19", "created_at": "2025-12-23T08:53:48-03:00", "likes": 39, "retweets": 5, "replies": 4, "views": 1810, "is_retweet": false, "is_reply": false, "media_type": null }, { "tweet_id": "2003432876595876172", "handle": "gradients_ai", "text": "1/2\n\nGradients takes the best decentralized, open-source base LLM from Templar Covenant and finetunes it into a chatbot assistant that can carry multi-turn conversation and reasonably respond to user queries, here's how we did it:\n\n- Chat template integration and embedding update\n- YaRN context window extension from 2k to 32k\n- Finetuning on a mix of SOTA open source datasets built to improve open source finetuning to destroy benchmarks, but augmented using our own synthetic few shot enhancement pipeline\n\nThe results speak for themselves: \nGradients training slashed test loss by 75% in under one epoch. This dramatic improvement shows how efficiently our autoML training pipeline extracts performance from your models.", "created_at": "2025-12-23T08:49:54-03:00", "likes": 73, "retweets": 17, "replies": 1, "views": 8429, "is_retweet": false, "is_reply": false, "media_type": null }, { "tweet_id": "2001416899117056054", "handle": "gradients_ai", "text": "Great collab with Templar's SN3 to post-train their 72B model! \nUsing the best autoML scripts on https://t.co/ArqoyWjn8D we:\n\n- introduced a chat template and fine-tuned on it\n- extended the context from 2k to a much more usable 32k \n- trained on multi-turn data and enabled the model to carry on conversations and introduced reasoning\n\nOur users can already fine-tune their models on a single dataset on Gradients... now this beast of a pipeline for much larger post-training runs is getting released on the platform so anyone can do the above with a few clicks", "created_at": "2025-12-17T19:19:07-03:00", "likes": 53, "retweets": 7, "replies": 6, "views": 6746, "is_retweet": false, "is_reply": false, "media_type": null }, { "tweet_id": "1994882244675043453", "handle": "gradients_ai", "text": "Dominos falling https://t.co/ASceK0y2W7", "created_at": "2025-11-29T18:32:44-03:00", "likes": 48, "retweets": 10, "replies": 2, "views": 2230, "is_retweet": false, "is_reply": false, "media_type": null }, { "tweet_id": "1993696703598227875", "handle": "gradients_ai", "text": "Gradients Instruct V2. \n\nQwen 32B? Beaten. \n\nOpen competition beats closed labs.\n\nhttps://t.co/E2cIWdGkgY https://t.co/PXqqzl0CPq", "created_at": "2025-11-26T12:01:49-03:00", "likes": 55, "retweets": 8, "replies": 2, "views": 1948, "is_retweet": false, "is_reply": false, "media_type": null }, { "tweet_id": "1993348090367996056", "handle": "gradients_ai", "text": "Introducing Gradients Instruct V2. \n\nWe don't cherry-pick benchmarks. \n\nWe take on them all and win.\n\nHave a play: https://t.co/nWyiAoZvho", "created_at": "2025-11-25T12:56:34-03:00", "likes": 29, "retweets": 6, "replies": 1, "views": 1119, "is_retweet": false, "is_reply": false, "media_type": null }, { "tweet_id": "1990015943431319830", "handle": "gradients_ai", "text": "$9-30 billion spent annually on ML engineers doing fine-tuning work.\n\nWe automate it at 60-80% cost reduction.\n\nWith 11-42% better performance.\n\nThe labor replacement opportunity is massive, and we're just getting started.\n\nFull breakdown \ud83d\udc47\nhttps://t.co/q5dI037phG", "created_at": "2025-11-16T08:15:48-03:00", "likes": 111, "retweets": 17, "replies": 4, "views": 9393, "is_retweet": false, "is_reply": false, "media_type": null }, { "tweet_id": "1989327988035289580", "handle": "gradients_ai", "text": "The companies that won with open-source all followed the same pattern: \n\nMake the tool ubiquitous \n\u2192 Build brand recognition \n\u2192 Monetise expertise \n\nRed Hat ($34B IBM acquisition): \nFree Linux, paid enterprise support \n\nMongoDB ($1.9B revenue): \nFree database, paid Atlas hosting + consulting \n\nGitLab ($15B valuation): \nFree DevOps tool, paid enterprise + services \n\nOur play: \n\u2192 Open tournament scripts (Apache 2.0 + attribution) \u2192 Gradients brand on every model trained \n\u2192 Consultation for data prep and implementation \n\u2192 Managed hosting for enterprises (infrastructure already built, prices lower than competitors) \n\nWe have the expertise. We have the infrastructure. We're already cheaper and more performant than HuggingFace, Databricks, Google Vertex. \n\nThe code is the marketing. The expertise is the business. \n\nFull breakdown: https://t.co/q5dI037phG", "created_at": "2025-11-14T10:42:06-03:00", "likes": 57, "retweets": 11, "replies": 0, "views": 8722, "is_retweet": false, "is_reply": false, "media_type": null }, { "tweet_id": "1987885450694967363", "handle": "gradients_ai", "text": "\ud83e\uddfe Gradients: The Path Forward - Available Now \n\nhttps://t.co/q5dI037phG", "created_at": "2025-11-10T11:09:59-03:00", "likes": 49, "retweets": 10, "replies": 1, "views": 1611, "is_retweet": false, "is_reply": false, "media_type": null }, { "tweet_id": "1986766607759118479", "handle": "gradients_ai", "text": "400,000-600,000 ML engineers globally spend 15-25% of their time fine-tuning models.\n\nThat's $9+ billion in annual labor spend.\n\nAutoML can deliver 50-80% time reduction.\n\nThe replacement opportunity and how Gradients captures it - full breakdown in 3 days. https://t.co/kOb75aIfwP", "created_at": "2025-11-07T09:04:06-03:00", "likes": 60, "retweets": 14, "replies": 2, "views": 2830, "is_retweet": false, "is_reply": false, "media_type": null }, { "tweet_id": "1986414831461683510", "handle": "gradients_ai", "text": "The LLM post-training market: $2-4B today, $12-20B by 2030. \n\nEnterprises face an impossible choice: hire ML engineers at $150k-500k each, or accept mediocre AutoML results. \n\nGradients solves this. \n\nFull breakdown doc hits in 4 days. \n\nhttps://t.co/E2cIWdGkgY https://t.co/OsHibfymi9", "created_at": "2025-11-06T09:46:16-03:00", "likes": 66, "retweets": 8, "replies": 5, "views": 1623, "is_retweet": false, "is_reply": false, "media_type": null } ] }