{
  "netuid": 114,
  "slug": "soma",
  "name": "SOMA",
  "symbol": "\u0404",
  "description": "AI solutions delivered through MCP infrastructure",
  "priceTao": 0.010251156,
  "priceUsd": 2.594066360673266,
  "athUsd": 2.594066360673266,
  "change24h": 0.30031910494848474,
  "change7d": 4.642350322680616,
  "change30d": -5.029461634283667,
  "change90d": 2.14,
  "marketCapUsd": 1598205.034542427,
  "volume24hUsd": 132773.37327139144,
  "rootProp": 0.5206120640027182,
  "liquidityUsd": 940812.7704341676,
  "alphaStaked": 434695.051020174,
  "activeMiners": 59,
  "activeValidators": 10,
  "emissionPct": 0.01583345692494584,
  "emissionPerDayTao": 1.0,
  "registrationCost": 307.770370853,
  "pruningRank": 104,
  "immunityRemaining": 4791,
  "sentiment": "Greed",
  "githubRepo": "https://github.com/DendriteHQ/SOMA",
  "subnetUrl": "https://thesoma.ai",
  "scorecard": {
    "adoption": {
      "label": "weak",
      "score": 18,
      "reasons": [
        {
          "tone": "neutral",
          "label": "Stars",
          "value": "5",
          "weight": null
        },
        {
          "tone": "neutral",
          "label": "Contributors",
          "value": "7",
          "weight": null
        },
        {
          "tone": "neutral",
          "label": "24h volume",
          "value": "$132,773",
          "weight": null
        },
        {
          "tone": "neutral",
          "label": "Validators",
          "value": "10",
          "weight": null
        },
        {
          "tone": "positive",
          "label": "Bus factor",
          "value": "Top contributor: 41%",
          "weight": null
        },
        {
          "tone": "neutral",
          "label": "LLM blend",
          "value": "heuristic=12 \u00d7 0.6 + llm=28 \u00d7 0.4",
          "weight": null
        }
      ]
    },
    "shipSpeed": {
      "label": "high",
      "score": 81,
      "reasons": [
        {
          "tone": "positive",
          "label": "Commits 30d",
          "value": "64",
          "weight": null
        },
        {
          "tone": "positive",
          "label": "Merged PRs 30d",
          "value": "23",
          "weight": null
        },
        {
          "tone": "neutral",
          "label": "Releases 12m",
          "value": "0",
          "weight": null
        },
        {
          "tone": "positive",
          "label": "Latest push",
          "value": "2026-04-28T15:09:52Z",
          "weight": null
        },
        {
          "tone": "negative",
          "label": "Momentum",
          "value": "decelerating (64/30d vs 97/mo avg)",
          "weight": null
        },
        {
          "tone": "neutral",
          "label": "LLM blend",
          "value": "heuristic=90 \u00d7 0.6 + llm=68 \u00d7 0.4",
          "weight": null
        }
      ]
    },
    "updatedAt": "2026-04-29T20:41:08.737632+00:00",
    "confidence": 0.82,
    "usefulness": {
      "label": "watch",
      "score": 43,
      "reasons": [
        {
          "tone": "neutral",
          "label": "Durable keywords",
          "value": "0 matched",
          "weight": null
        },
        {
          "tone": "positive",
          "label": "Commodity risk",
          "value": "0 matched",
          "weight": null
        },
        {
          "tone": "neutral",
          "label": "Subnet traction",
          "value": "0/30",
          "weight": null
        },
        {
          "tone": "neutral",
          "label": "LLM blend",
          "value": "heuristic=30 \u00d7 0.6 + llm=63 \u00d7 0.4",
          "weight": null
        }
      ]
    },
    "codeQuality": {
      "label": "weak",
      "score": 30,
      "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": "neutral",
          "label": "PR reviews",
          "value": "1.1 avg, 90% self-merge",
          "weight": null
        },
        {
          "tone": "positive",
          "label": "Commit quality",
          "value": "50% conventional",
          "weight": null
        },
        {
          "tone": "neutral",
          "label": "Security hygiene",
          "value": "No SECURITY.md",
          "weight": null
        },
        {
          "tone": "neutral",
          "label": "LLM blend",
          "value": "heuristic=30 \u00d7 0.6 + llm=30 \u00d7 0.4",
          "weight": null
        }
      ]
    }
  },
  "marketScore": 13.0,
  "githubScore": 52.0,
  "aiScore": 49.0,
  "masterScore": 37.0,
  "masterRating": "C",
  "lastUpdatedAt": "2026-04-29T20:21:48Z",
  "isStale": false,
  "masterScorePrev": 35.0,
  "purpose": {
    "title": "SOMA",
    "source": "llm",
    "confidence": 0.72,
    "shortSummary": "SOMA (netuid 114) integrates MCP (Model Context Protocol) servers into Bittensor, creating a decentralized marketplace where miners compete on availability, latency, and quality of AI tool-access services. The infrastructure concept is genuinely useful and timely given MCP's rapid adoption, but the codebase shows serious review hygiene issues (0.90 self-merge ratio) and community traction remains thin at 5 GitHub stars. Active development is strong at 64 commits and 23 merged PRs in the last 30 days, but no formal releases have shipped in 12 months."
  },
  "analysis": {
    "risks": [
      "Self-merge ratio of 0.90 is a critical red flag \u2014 nearly all PRs are merged by the author without independent review, meaning bugs and design flaws pass unchecked despite averaging 1.1 reviews per PR on paper",
      "Community adoption is extremely thin (5 stars, 4 forks, $1.6M market cap, zero formal releases in 12 months), suggesting the subnet has not yet demonstrated value to external developers or institutional validators"
    ],
    "score": 49,
    "summary": "SOMA (netuid 114) integrates MCP (Model Context Protocol) servers into Bittensor, creating a decentralized marketplace where miners compete on availability, latency, and quality of AI tool-access services. The infrastructure concept is genuinely useful and timely given MCP's rapid adoption, but the codebase shows serious review hygiene issues (0.90 self-merge ratio) and community traction remains thin at 5 GitHub stars. Active development is strong at 64 commits and 23 merged PRs in the last 30 days, but no formal releases have shipped in 12 months.",
    "provider": "cascade:sonnet",
    "expiresAt": "2026-05-29T20:39:14.187517+00:00",
    "strengths": [
      "Strong development velocity: 64 commits and 23 merged PRs in the last 30 days, with 291 commits over 90 days showing sustained activity",
      "MCP infrastructure addresses a real and growing demand \u2014 enabling AI models to interact with external tools via a decentralized, incentivized network is a differentiated capability",
      "59 active miners and 10 validators indicate a functioning competitive network with real participation, not a ghost chain"
    ],
    "updatedAt": "2026-04-29T20:39:14.187517+00:00",
    "durability": "watch",
    "recommendation": "Monitor for the next 60 days: if SOMA ships a formal release, adds CI, and drops the self-merge ratio below 0.6, it becomes a buy \u2014 otherwise the code quality deficit will compound as the codebase scales.",
    "dimensionScores": {
      "adoption": 28,
      "shipSpeed": 68,
      "usefulness": 63,
      "codeQuality": 30
    }
  },
  "repoCoverageStatus": "covered",
  "image": "https://thesoma.ai/images/1200x630.png",
  "repos": [
    {
      "netuid": 114,
      "repoKind": "primary",
      "provider": "github",
      "owner": "DendriteHQ",
      "name": "SOMA",
      "url": "https://github.com/DendriteHQ/SOMA",
      "source": "registry",
      "isArchived": false,
      "isFork": false
    }
  ],
  "repoSnapshots": [
    {
      "fullName": "DendriteHQ/SOMA",
      "description": null,
      "homepageUrl": null,
      "defaultBranch": "main",
      "stars": 5,
      "forks": 4,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 64,
      "commits90d": 291,
      "mergedPrs30d": 23,
      "mergedPrs90d": 95,
      "releases12m": 0,
      "openIssues": 2,
      "closedIssues30d": 0,
      "languages": [
        {
          "name": "Python",
          "percentage": 98.4
        },
        {
          "name": "Shell",
          "percentage": 1.4
        },
        {
          "name": "Dockerfile",
          "percentage": 0.2
        }
      ],
      "topics": [],
      "rootSignals": {
        "hasCi": false,
        "hasTests": true,
        "hasLintConfig": false,
        "hasFormatterConfig": false,
        "hasTypeScript": false,
        "hasLicense": true,
        "hasSecurityPolicy": false,
        "hasDocs": true,
        "hasDocker": false,
        "hasDependabot": false,
        "hasRenovate": false,
        "rootEntries": [
          ".gitignore",
          "LICENSE",
          "README.md",
          "docs",
          "mcp_platform",
          "miner",
          "pytest.ini",
          "requirements.txt",
          "sandbox_service",
          "validator"
        ]
      },
      "readmeExcerpt": "SOMA ! SOMA docs/images/SOMA.jpg Overview This subnet brings **MCP Model Context Protocol servers** into the Bittensor ecosystem, enabling AI models to securely interact with external tools, data sources, and execution environments. By combining MCP with Bittensor's incentive-driven design, the subnet creates a competitive environment where miners are rewarded for delivering **high-availability, low-latency, and high-quality MCP services**. Vision Our goal is to build a decentralized platform of production-ready MCP servers that: - extend AI models with real-world capabilities through standard",
      "pushedAt": "2026-04-28T15:09:52Z",
      "isArchived": false,
      "isFork": false,
      "commitMessageQuality": 0.5,
      "selfMergeRatio": 0.9,
      "avgReviewsPerPr": 1.1,
      "hasBranchProtection": false,
      "topContributorPct": 0.41,
      "avgCommentsPerIssue": 0.0,
      "avgResponseHours": 0.0,
      "prMergeHoursP50": 1.5,
      "prMergeHoursP95": 40.6,
      "contributorChurn30d": 0.0,
      "vulnerabilityCount": 0
    }
  ],
  "externalLinks": [
    {
      "label": "Website",
      "href": "https://thesoma.ai"
    },
    {
      "label": "GitHub",
      "href": "https://github.com/DendriteHQ/SOMA"
    }
  ],
  "priceHistory": [
    {
      "timestamp": "2026-03-09T13:50:00-03:00",
      "value": 3.67232362
    },
    {
      "timestamp": "2026-03-09T19:24:36-03:00",
      "value": 2.83388112
    },
    {
      "timestamp": "2026-03-09T19:25:48-03:00",
      "value": 2.83386693
    },
    {
      "timestamp": "2026-03-09T19:42:12-03:00",
      "value": 2.79132072
    },
    {
      "timestamp": "2026-03-09T19:54:48-03:00",
      "value": 2.69322235
    },
    {
      "timestamp": "2026-03-10T22:04:12-03:00",
      "value": 3.80518925
    },
    {
      "timestamp": "2026-03-11T04:57:36.001000-03:00",
      "value": 3.47627958
    },
    {
      "timestamp": "2026-03-15T09:16:36-03:00",
      "value": 3.42022342
    },
    {
      "timestamp": "2026-03-16T15:03:36-03:00",
      "value": 3.70749153
    },
    {
      "timestamp": "2026-03-18T13:29:12-03:00",
      "value": 3.26910655
    },
    {
      "timestamp": "2026-03-18T19:54:00.001000-03:00",
      "value": 3.13102293
    },
    {
      "timestamp": "2026-03-19T10:05:36-03:00",
      "value": 2.82232215
    },
    {
      "timestamp": "2026-03-20T01:19:36-03:00",
      "value": 3.15434069
    },
    {
      "timestamp": "2026-03-21T15:01:36.001000-03:00",
      "value": 3.65744025
    },
    {
      "timestamp": "2026-03-21T16:30:36-03:00",
      "value": 3.62980276
    },
    {
      "timestamp": "2026-03-22T19:15:12.001000-03:00",
      "value": 3.71627238
    },
    {
      "timestamp": "2026-03-22T20:00:24-03:00",
      "value": 3.72400372
    },
    {
      "timestamp": "2026-03-22T20:11:48-03:00",
      "value": 3.72361665
    },
    {
      "timestamp": "2026-03-22T20:12:24-03:00",
      "value": 3.72360846
    },
    {
      "timestamp": "2026-03-23T18:36:36-03:00",
      "value": 3.89563885
    },
    {
      "timestamp": "2026-03-23T20:58:48.001000-03:00",
      "value": 3.91165987
    },
    {
      "timestamp": "2026-03-23T22:10:36-03:00",
      "value": 4.05576468
    },
    {
      "timestamp": "2026-03-24T18:03:36-03:00",
      "value": 4.18897609
    },
    {
      "timestamp": "2026-03-24T18:59:36.001000-03:00",
      "value": 4.42411942
    },
    {
      "timestamp": "2026-03-25T16:57:00-03:00",
      "value": 4.60362738
    },
    {
      "timestamp": "2026-03-26T18:45:00.001000-03:00",
      "value": 3.95704267
    },
    {
      "timestamp": "2026-03-27T17:39:36-03:00",
      "value": 4.00117768
    },
    {
      "timestamp": "2026-03-30T15:18:12-03:00",
      "value": 3.27386616
    },
    {
      "timestamp": "2026-03-31T20:00:24-03:00",
      "value": 2.98402361
    },
    {
      "timestamp": "2026-04-02T03:21:48.001000-03:00",
      "value": 2.71210279
    },
    {
      "timestamp": "2026-04-02T17:46:36-03:00",
      "value": 2.80565176
    },
    {
      "timestamp": "2026-04-03T11:49:48.001000-03:00",
      "value": 2.89956263
    },
    {
      "timestamp": "2026-04-06T19:08:00-03:00",
      "value": 3.06776165
    },
    {
      "timestamp": "2026-04-07T02:41:12-03:00",
      "value": 3.01953271
    },
    {
      "timestamp": "2026-04-07T19:13:00-03:00",
      "value": 3.35593285
    },
    {
      "timestamp": "2026-04-10T18:50:24-03:00",
      "value": 2.57199904
    },
    {
      "timestamp": "2026-04-14T18:45:36.001000-03:00",
      "value": 2.17051139
    },
    {
      "timestamp": "2026-04-16T06:16:12-03:00",
      "value": 2.19177063
    },
    {
      "timestamp": "2026-04-16T14:43:12-03:00",
      "value": 2.24235026
    },
    {
      "timestamp": "2026-04-17T04:46:24.001000-03:00",
      "value": 2.59218297
    },
    {
      "timestamp": "2026-04-19T05:53:36-03:00",
      "value": 2.34332411
    },
    {
      "timestamp": "2026-04-22T01:33:12-03:00",
      "value": 2.6396163
    },
    {
      "timestamp": "2026-04-22T09:11:24-03:00",
      "value": 2.49907308
    },
    {
      "timestamp": "2026-04-23T11:01:24-03:00",
      "value": 2.42000308
    },
    {
      "timestamp": "2026-04-24T13:20:00-03:00",
      "value": 2.39621069
    },
    {
      "timestamp": "2026-04-24T13:22:12.001000-03:00",
      "value": 2.39624798
    },
    {
      "timestamp": "2026-04-25T13:29:24-03:00",
      "value": 2.39728542
    },
    {
      "timestamp": "2026-04-26T17:26:48-03:00",
      "value": 2.36320094
    },
    {
      "timestamp": "2026-04-29T17:21:48-03:00",
      "value": 2.59406636
    }
  ],
  "liquidityHistory": [
    {
      "timestamp": "2026-03-09T13:50:00-03:00",
      "value": 235694.1216839
    },
    {
      "timestamp": "2026-03-09T19:24:36-03:00",
      "value": 209179.46231539
    },
    {
      "timestamp": "2026-03-09T19:25:48-03:00",
      "value": 209186.48150953
    },
    {
      "timestamp": "2026-03-09T19:42:12-03:00",
      "value": 207713.25294547
    },
    {
      "timestamp": "2026-03-09T19:54:48-03:00",
      "value": 204109.91132323
    },
    {
      "timestamp": "2026-03-10T22:04:12-03:00",
      "value": 258776.72541666
    },
    {
      "timestamp": "2026-03-11T04:57:36.001000-03:00",
      "value": 246383.96621128
    },
    {
      "timestamp": "2026-03-15T09:16:36-03:00",
      "value": 337158.76050051
    },
    {
      "timestamp": "2026-03-16T15:03:36-03:00",
      "value": 379483.92428531
    },
    {
      "timestamp": "2026-03-18T13:29:12-03:00",
      "value": 370956.77225958
    },
    {
      "timestamp": "2026-03-18T19:54:00.001000-03:00",
      "value": 366358.13130134
    },
    {
      "timestamp": "2026-03-19T10:05:36-03:00",
      "value": 338543.50624346
    },
    {
      "timestamp": "2026-03-20T01:19:36-03:00",
      "value": 400050.45145493
    },
    {
      "timestamp": "2026-03-21T15:01:36.001000-03:00",
      "value": 434980.69763841
    },
    {
      "timestamp": "2026-03-21T16:30:36-03:00",
      "value": 434112.45274983
    },
    {
      "timestamp": "2026-03-22T19:15:12.001000-03:00",
      "value": 446085.41186062
    },
    {
      "timestamp": "2026-03-22T20:00:24-03:00",
      "value": 446942.18773738
    },
    {
      "timestamp": "2026-03-22T20:11:48-03:00",
      "value": 447018.07928166
    },
    {
      "timestamp": "2026-03-22T20:12:24-03:00",
      "value": 447022.80644566
    },
    {
      "timestamp": "2026-03-23T18:36:36-03:00",
      "value": 490649.92281752
    },
    {
      "timestamp": "2026-03-23T20:58:48.001000-03:00",
      "value": 495667.19152985
    },
    {
      "timestamp": "2026-03-23T22:10:36-03:00",
      "value": 511991.86219175
    },
    {
      "timestamp": "2026-03-24T18:03:36-03:00",
      "value": 557326.4623128
    },
    {
      "timestamp": "2026-03-24T18:59:36.001000-03:00",
      "value": 570406.02288701
    },
    {
      "timestamp": "2026-03-25T16:57:00-03:00",
      "value": 615568.52848464
    },
    {
      "timestamp": "2026-03-26T18:45:00.001000-03:00",
      "value": 573972.49250316
    },
    {
      "timestamp": "2026-03-27T17:39:36-03:00",
      "value": 575903.95231299
    },
    {
      "timestamp": "2026-03-30T15:18:12-03:00",
      "value": 550676.51800405
    },
    {
      "timestamp": "2026-03-31T20:00:24-03:00",
      "value": 542840.23545484
    },
    {
      "timestamp": "2026-04-02T03:21:48.001000-03:00",
      "value": 535593.23624489
    },
    {
      "timestamp": "2026-04-02T17:46:36-03:00",
      "value": 548985.77949815
    },
    {
      "timestamp": "2026-04-03T11:49:48.001000-03:00",
      "value": 579183.74768927
    },
    {
      "timestamp": "2026-04-06T19:08:00-03:00",
      "value": 650253.90665337
    },
    {
      "timestamp": "2026-04-07T02:41:12-03:00",
      "value": 646292.21499018
    },
    {
      "timestamp": "2026-04-07T19:13:00-03:00",
      "value": 709727.23393966
    },
    {
      "timestamp": "2026-04-10T18:50:24-03:00",
      "value": 608045.63328412
    },
    {
      "timestamp": "2026-04-14T18:45:36.001000-03:00",
      "value": 597954.80120669
    },
    {
      "timestamp": "2026-04-16T06:16:12-03:00",
      "value": 622393.29829015
    },
    {
      "timestamp": "2026-04-16T14:43:12-03:00",
      "value": 631671.58671156
    },
    {
      "timestamp": "2026-04-17T04:46:24.001000-03:00",
      "value": 700503.01382071
    },
    {
      "timestamp": "2026-04-19T05:53:36-03:00",
      "value": 700792.44083804
    },
    {
      "timestamp": "2026-04-22T01:33:12-03:00",
      "value": 797932.7913655
    },
    {
      "timestamp": "2026-04-22T09:11:24-03:00",
      "value": 782926.76539418
    },
    {
      "timestamp": "2026-04-23T11:01:24-03:00",
      "value": 784080.47235298
    },
    {
      "timestamp": "2026-04-24T13:20:00-03:00",
      "value": 804429.99186782
    },
    {
      "timestamp": "2026-04-24T13:22:12.001000-03:00",
      "value": 804462.61361121
    },
    {
      "timestamp": "2026-04-25T13:29:24-03:00",
      "value": 824551.81948338
    },
    {
      "timestamp": "2026-04-26T17:26:48-03:00",
      "value": 843180.13267743
    },
    {
      "timestamp": "2026-04-29T17:21:48-03:00",
      "value": 940812.77043417
    }
  ],
  "scoreHistory": [
    {
      "date": "2026-03-09",
      "masterScore": 69.0,
      "marketScore": 90.0,
      "githubScore": 50.0,
      "aiScore": 70.0,
      "socialScore": 0.0,
      "rating": "accumulating",
      "usefulness": 73,
      "codeQuality": 32,
      "adoption": 54,
      "shipSpeed": 90
    },
    {
      "date": "2026-03-10",
      "masterScore": 69.0,
      "marketScore": 90.0,
      "githubScore": 50.0,
      "aiScore": 70.0,
      "socialScore": 0.0,
      "rating": "accumulating",
      "usefulness": 73,
      "codeQuality": 32,
      "adoption": 54,
      "shipSpeed": 90
    },
    {
      "date": "2026-03-15",
      "masterScore": 70.0,
      "marketScore": 90.0,
      "githubScore": 52.0,
      "aiScore": 70.0,
      "socialScore": 0.0,
      "rating": "accumulating",
      "usefulness": 64,
      "codeQuality": 28,
      "adoption": 46,
      "shipSpeed": 78
    },
    {
      "date": "2026-03-18",
      "masterScore": 59.0,
      "marketScore": 90.0,
      "githubScore": 52.0,
      "aiScore": 70.0,
      "socialScore": 0.0,
      "rating": "B",
      "usefulness": 64,
      "codeQuality": 29,
      "adoption": 46,
      "shipSpeed": 78
    },
    {
      "date": "2026-03-19",
      "masterScore": 54.0,
      "marketScore": 90.0,
      "githubScore": 52.0,
      "aiScore": 44.0,
      "socialScore": 0.0,
      "rating": "B",
      "usefulness": 62,
      "codeQuality": 29,
      "adoption": 44,
      "shipSpeed": 75
    },
    {
      "date": "2026-03-20",
      "masterScore": 60.0,
      "marketScore": 91.0,
      "githubScore": 52.0,
      "aiScore": 70.0,
      "socialScore": 0.0,
      "rating": "B",
      "usefulness": 64,
      "codeQuality": 29,
      "adoption": 46,
      "shipSpeed": 78
    },
    {
      "date": "2026-03-21",
      "masterScore": 55.0,
      "marketScore": 91.0,
      "githubScore": 52.0,
      "aiScore": 46.0,
      "socialScore": 0.0,
      "rating": "B",
      "usefulness": 62,
      "codeQuality": 31,
      "adoption": 45,
      "shipSpeed": 76
    },
    {
      "date": "2026-03-22",
      "masterScore": 63.0,
      "marketScore": 91.0,
      "githubScore": 52.0,
      "aiScore": 40.0,
      "socialScore": 0.0,
      "rating": "B",
      "usefulness": 54,
      "codeQuality": 31,
      "adoption": 48,
      "shipSpeed": 73
    },
    {
      "date": "2026-03-23",
      "masterScore": 58.0,
      "marketScore": 90.0,
      "githubScore": 52.0,
      "aiScore": 42.0,
      "socialScore": 30.0,
      "rating": "B",
      "usefulness": 58,
      "codeQuality": 30,
      "adoption": 41,
      "shipSpeed": 80
    },
    {
      "date": "2026-03-24",
      "masterScore": 40.0,
      "marketScore": 21.0,
      "githubScore": 53.0,
      "aiScore": 51.0,
      "socialScore": 31.0,
      "rating": "C",
      "usefulness": 44,
      "codeQuality": 35,
      "adoption": 21,
      "shipSpeed": 79
    },
    {
      "date": "2026-03-25",
      "masterScore": 39.0,
      "marketScore": 22.0,
      "githubScore": 53.0,
      "aiScore": 45.0,
      "socialScore": 31.0,
      "rating": "C",
      "usefulness": 39,
      "codeQuality": 31,
      "adoption": 18,
      "shipSpeed": 82
    },
    {
      "date": "2026-03-26",
      "masterScore": 38.0,
      "marketScore": 20.0,
      "githubScore": 53.0,
      "aiScore": 44.0,
      "socialScore": 32.0,
      "rating": "C",
      "usefulness": 39,
      "codeQuality": 33,
      "adoption": 17,
      "shipSpeed": 81
    },
    {
      "date": "2026-03-27",
      "masterScore": 38.0,
      "marketScore": 20.0,
      "githubScore": 53.0,
      "aiScore": 41.0,
      "socialScore": 34.0,
      "rating": "C",
      "usefulness": 39,
      "codeQuality": 34,
      "adoption": 18,
      "shipSpeed": 71
    },
    {
      "date": "2026-03-30",
      "masterScore": 44.0,
      "marketScore": 21.0,
      "githubScore": 52.0,
      "aiScore": 70.0,
      "socialScore": 34.0,
      "rating": "C",
      "usefulness": 46,
      "codeQuality": 27,
      "adoption": 11,
      "shipSpeed": 81
    },
    {
      "date": "2026-04-01",
      "masterScore": 39.0,
      "marketScore": 21.0,
      "githubScore": 52.0,
      "aiScore": 48.0,
      "socialScore": 34.0,
      "rating": "C",
      "usefulness": 45,
      "codeQuality": 30,
      "adoption": 19,
      "shipSpeed": 75
    },
    {
      "date": "2026-04-02",
      "masterScore": 39.0,
      "marketScore": 19.0,
      "githubScore": 53.0,
      "aiScore": 47.0,
      "socialScore": 34.0,
      "rating": "C",
      "usefulness": 41,
      "codeQuality": 28,
      "adoption": 18,
      "shipSpeed": 87
    },
    {
      "date": "2026-04-03",
      "masterScore": 44.0,
      "marketScore": 20.0,
      "githubScore": 53.0,
      "aiScore": 70.0,
      "socialScore": 34.0,
      "rating": "C",
      "usefulness": 46,
      "codeQuality": 27,
      "adoption": 12,
      "shipSpeed": 84
    },
    {
      "date": "2026-04-07",
      "masterScore": 34.0,
      "marketScore": 6.0,
      "githubScore": 53.0,
      "aiScore": 44.0,
      "socialScore": 33.0,
      "rating": "D",
      "usefulness": 40,
      "codeQuality": 30,
      "adoption": 17,
      "shipSpeed": 84
    },
    {
      "date": "2026-04-08",
      "masterScore": 36.0,
      "marketScore": 11.0,
      "githubScore": 53.0,
      "aiScore": 44.0,
      "socialScore": 33.0,
      "rating": "C",
      "usefulness": 37,
      "codeQuality": 28,
      "adoption": 22,
      "shipSpeed": 83
    },
    {
      "date": "2026-04-11",
      "masterScore": 38.0,
      "marketScore": 14.0,
      "githubScore": 54.0,
      "aiScore": 51.0,
      "socialScore": 32.0,
      "rating": "C",
      "usefulness": 44,
      "codeQuality": 27,
      "adoption": 22,
      "shipSpeed": 87
    },
    {
      "date": "2026-04-15",
      "masterScore": 30.0,
      "marketScore": 6.0,
      "githubScore": 52.0,
      "aiScore": 30.0,
      "socialScore": 30.0,
      "rating": "D",
      "usefulness": 32,
      "codeQuality": 27,
      "adoption": 15,
      "shipSpeed": 70
    },
    {
      "date": "2026-04-16",
      "masterScore": 35.0,
      "marketScore": 6.0,
      "githubScore": 52.0,
      "aiScore": 49.0,
      "socialScore": 31.0,
      "rating": "C",
      "usefulness": 41,
      "codeQuality": 31,
      "adoption": 19,
      "shipSpeed": 84
    },
    {
      "date": "2026-04-17",
      "masterScore": 35.0,
      "marketScore": 9.0,
      "githubScore": 52.0,
      "aiScore": 49.0,
      "socialScore": 30.0,
      "rating": "C",
      "usefulness": 43,
      "codeQuality": 28,
      "adoption": 18,
      "shipSpeed": 86
    },
    {
      "date": "2026-04-19",
      "masterScore": 34.0,
      "marketScore": 6.0,
      "githubScore": 52.0,
      "aiScore": 46.0,
      "socialScore": 30.0,
      "rating": "D",
      "usefulness": 43,
      "codeQuality": 28,
      "adoption": 16,
      "shipSpeed": 81
    },
    {
      "date": "2026-04-22",
      "masterScore": 38.0,
      "marketScore": 18.0,
      "githubScore": 52.0,
      "aiScore": 51.0,
      "socialScore": 28.0,
      "rating": "C",
      "usefulness": 43,
      "codeQuality": 31,
      "adoption": 20,
      "shipSpeed": 86
    },
    {
      "date": "2026-04-23",
      "masterScore": 37.0,
      "marketScore": 13.0,
      "githubScore": 53.0,
      "aiScore": 51.0,
      "socialScore": 28.0,
      "rating": "C",
      "usefulness": 43,
      "codeQuality": 29,
      "adoption": 19,
      "shipSpeed": 86
    },
    {
      "date": "2026-04-24",
      "masterScore": 35.0,
      "marketScore": 8.0,
      "githubScore": 53.0,
      "aiScore": 49.0,
      "socialScore": 30.0,
      "rating": "C",
      "usefulness": 43,
      "codeQuality": 31,
      "adoption": 18,
      "shipSpeed": 84
    },
    {
      "date": "2026-04-25",
      "masterScore": 36.0,
      "marketScore": 9.0,
      "githubScore": 53.0,
      "aiScore": 50.0,
      "socialScore": 31.0,
      "rating": "C",
      "usefulness": 43,
      "codeQuality": 29,
      "adoption": 20,
      "shipSpeed": 84
    },
    {
      "date": "2026-04-26",
      "masterScore": 35.0,
      "marketScore": 10.0,
      "githubScore": 51.0,
      "aiScore": 48.0,
      "socialScore": 31.0,
      "rating": "C",
      "usefulness": 41,
      "codeQuality": 31,
      "adoption": 19,
      "shipSpeed": 80
    },
    {
      "date": "2026-04-29",
      "masterScore": 37.0,
      "marketScore": 13.0,
      "githubScore": 52.0,
      "aiScore": 49.0,
      "socialScore": 31.0,
      "rating": "C",
      "usefulness": 43,
      "codeQuality": 30,
      "adoption": 18,
      "shipSpeed": 81
    }
  ],
  "githubHistory": [
    {
      "date": "2026-03-09",
      "stars": 3,
      "forks": 3,
      "watchers": 0,
      "contributors": 5,
      "commits30d": 93,
      "commits90d": 93,
      "mergedPrs30d": 32,
      "mergedPrs90d": 32,
      "releases12m": 0,
      "openIssues": 2,
      "closedIssues30d": 0
    },
    {
      "date": "2026-03-10",
      "stars": 3,
      "forks": 3,
      "watchers": 1,
      "contributors": 5,
      "commits30d": 98,
      "commits90d": 98,
      "mergedPrs30d": 34,
      "mergedPrs90d": 34,
      "releases12m": 0,
      "openIssues": 1,
      "closedIssues30d": 0
    },
    {
      "date": "2026-03-15",
      "stars": 4,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 143,
      "commits90d": 143,
      "mergedPrs30d": 50,
      "mergedPrs90d": 50,
      "releases12m": 0,
      "openIssues": 1,
      "closedIssues30d": 0
    },
    {
      "date": "2026-03-18",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 146,
      "commits90d": 146,
      "mergedPrs30d": 54,
      "mergedPrs90d": 54,
      "releases12m": 0,
      "openIssues": 3,
      "closedIssues30d": 0
    },
    {
      "date": "2026-03-20",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 146,
      "commits90d": 146,
      "mergedPrs30d": 60,
      "mergedPrs90d": 60,
      "releases12m": 0,
      "openIssues": 2,
      "closedIssues30d": 0
    },
    {
      "date": "2026-03-21",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 146,
      "commits90d": 146,
      "mergedPrs30d": 62,
      "mergedPrs90d": 62,
      "releases12m": 0,
      "openIssues": 3,
      "closedIssues30d": 0
    },
    {
      "date": "2026-03-22",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 146,
      "commits90d": 146,
      "mergedPrs30d": 62,
      "mergedPrs90d": 62,
      "releases12m": 0,
      "openIssues": 3,
      "closedIssues30d": 0
    },
    {
      "date": "2026-03-23",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 181,
      "commits90d": 181,
      "mergedPrs30d": 66,
      "mergedPrs90d": 66,
      "releases12m": 0,
      "openIssues": 0,
      "closedIssues30d": 0
    },
    {
      "date": "2026-03-25",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 181,
      "commits90d": 181,
      "mergedPrs30d": 68,
      "mergedPrs90d": 68,
      "releases12m": 0,
      "openIssues": 2,
      "closedIssues30d": 0
    },
    {
      "date": "2026-03-26",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 181,
      "commits90d": 181,
      "mergedPrs30d": 69,
      "mergedPrs90d": 69,
      "releases12m": 0,
      "openIssues": 3,
      "closedIssues30d": 0
    },
    {
      "date": "2026-03-30",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 225,
      "commits90d": 226,
      "mergedPrs30d": 76,
      "mergedPrs90d": 76,
      "releases12m": 0,
      "openIssues": 1,
      "closedIssues30d": 0
    },
    {
      "date": "2026-03-31",
      "stars": 5,
      "forks": 4,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 225,
      "commits90d": 226,
      "mergedPrs30d": 76,
      "mergedPrs90d": 76,
      "releases12m": 0,
      "openIssues": 2,
      "closedIssues30d": 0
    },
    {
      "date": "2026-04-02",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 210,
      "commits90d": 226,
      "mergedPrs30d": 73,
      "mergedPrs90d": 79,
      "releases12m": 0,
      "openIssues": 0,
      "closedIssues30d": 0
    },
    {
      "date": "2026-04-03",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 181,
      "commits90d": 226,
      "mergedPrs30d": 70,
      "mergedPrs90d": 80,
      "releases12m": 0,
      "openIssues": 2,
      "closedIssues30d": 0
    },
    {
      "date": "2026-04-06",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 146,
      "commits90d": 226,
      "mergedPrs30d": 61,
      "mergedPrs90d": 80,
      "releases12m": 0,
      "openIssues": 2,
      "closedIssues30d": 0
    },
    {
      "date": "2026-04-07",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 146,
      "commits90d": 226,
      "mergedPrs30d": 55,
      "mergedPrs90d": 80,
      "releases12m": 0,
      "openIssues": 2,
      "closedIssues30d": 0
    },
    {
      "date": "2026-04-10",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 130,
      "commits90d": 251,
      "mergedPrs30d": 50,
      "mergedPrs90d": 84,
      "releases12m": 0,
      "openIssues": 1,
      "closedIssues30d": 0
    },
    {
      "date": "2026-04-14",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 106,
      "commits90d": 251,
      "mergedPrs30d": 34,
      "mergedPrs90d": 84,
      "releases12m": 0,
      "openIssues": 2,
      "closedIssues30d": 0
    },
    {
      "date": "2026-04-16",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 108,
      "commits90d": 255,
      "mergedPrs30d": 36,
      "mergedPrs90d": 86,
      "releases12m": 0,
      "openIssues": 1,
      "closedIssues30d": 0
    },
    {
      "date": "2026-04-19",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 78,
      "commits90d": 255,
      "mergedPrs30d": 28,
      "mergedPrs90d": 88,
      "releases12m": 0,
      "openIssues": 1,
      "closedIssues30d": 0
    },
    {
      "date": "2026-04-22",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 95,
      "commits90d": 275,
      "mergedPrs30d": 29,
      "mergedPrs90d": 91,
      "releases12m": 0,
      "openIssues": 2,
      "closedIssues30d": 0
    },
    {
      "date": "2026-04-23",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 82,
      "commits90d": 280,
      "mergedPrs30d": 26,
      "mergedPrs90d": 92,
      "releases12m": 0,
      "openIssues": 2,
      "closedIssues30d": 0
    },
    {
      "date": "2026-04-24",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 78,
      "commits90d": 280,
      "mergedPrs30d": 24,
      "mergedPrs90d": 92,
      "releases12m": 0,
      "openIssues": 2,
      "closedIssues30d": 0
    },
    {
      "date": "2026-04-25",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 69,
      "commits90d": 280,
      "mergedPrs30d": 24,
      "mergedPrs90d": 92,
      "releases12m": 0,
      "openIssues": 2,
      "closedIssues30d": 0
    },
    {
      "date": "2026-04-26",
      "stars": 5,
      "forks": 3,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 60,
      "commits90d": 280,
      "mergedPrs30d": 23,
      "mergedPrs90d": 92,
      "releases12m": 0,
      "openIssues": 2,
      "closedIssues30d": 0
    },
    {
      "date": "2026-04-29",
      "stars": 5,
      "forks": 4,
      "watchers": 1,
      "contributors": 7,
      "commits30d": 64,
      "commits90d": 291,
      "mergedPrs30d": 23,
      "mergedPrs90d": 95,
      "releases12m": 0,
      "openIssues": 2,
      "closedIssues30d": 0
    }
  ],
  "socialAccounts": [
    {
      "handle": "somasubnet",
      "role": "project",
      "label": null,
      "source": "taostats_identity",
      "confidence": 0.95,
      "profile_image_url": null
    }
  ],
  "recentTweets": [
    {
      "tweet_id": "2049186943003742381",
      "handle": "SomaSubnet",
      "text": "\ud83d\udea8 Plain-text compression is just the first step.\n\nOn May 4 at 14:00 UTC, the second SOMA competition begins: Agent Chain-of-Thought (CoT) Compression.\n\nAgent reasoning is where context costs compound the fastest. Every step replays the full trace of everything that came before. Compress that trace without breaking the agent, and every step becomes cheaper.\n\nThe dataset consists of real coding workloads. The validation is designed to reward compression that preserves task performance.\n\nLet\u2019s build something great together.",
      "created_at": "2026-04-28T15:00:13-03:00",
      "likes": 28,
      "retweets": 5,
      "replies": 2,
      "views": 566,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2049139431412486422",
      "handle": "SomaSubnet",
      "text": "Is this the moment for open-source agents with real session control?\n\nAs tools like GitHub Copilot move toward usage-based billing, every unnecessary token starts to matter.\n\nThis is the kind of problem SOMA is designed to solve: open infrastructure for context compression and agentic systems where session state is not just hidden magic, but something we can optimize, benchmark, and improve.\n\nBigger context windows help.\nBetter context management wins.",
      "created_at": "2026-04-28T11:51:26-03:00",
      "likes": 33,
      "retweets": 1,
      "replies": 0,
      "views": 809,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2047692058723262620",
      "handle": "SomaSubnet",
      "text": "\ud83d\udea8 Meet SOMARIZER (Beta) - the first tool powered by SOMA Subnet.\n\nStrip your text down to its core meaning. Fewer tokens, same context. You control how aggressive the compression gets.\n\nPaste raw text or drop in a PDF - both work out of the box.\n\nThis is SOMA leaving the benchmark phase: the compression algorithms our miners have been refining for weeks, now usable by anyone.\n\nTry it now: https://t.co/tpngU7rBGD",
      "created_at": "2026-04-24T12:00:05-03:00",
      "likes": 56,
      "retweets": 9,
      "replies": 1,
      "views": 1258,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2046921974891389321",
      "handle": "SomaSubnet",
      "text": "\ud83d\udea8 New feature live on SOMA\n\nMiners can now test the current top algorithm on their own inputs and inspect the compressed result.\n\nRun your own examples, review the result, and see for yourself whether the leader is producing meaningful work.\n\nCommunity request - now live. Version with UI coming soon. \n\nSign in as a tester: https://t.co/tpngU7rBGD\n\nDocs: https://t.co/p6uaKLkDfa",
      "created_at": "2026-04-22T09:00:03-03:00",
      "likes": 46,
      "retweets": 7,
      "replies": 1,
      "views": 2192,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2045155401642152070",
      "handle": "SomaSubnet",
      "text": "\ud83d\udea8 Agent chain-of-thought compression challenge incoming.\n\nWe're heads-down on two fronts:\n\u27a1\ufe0f preparing the dataset (real coding sessions)\n\u27a1\ufe0f designing the validation pipeline to make sure it measures what actually matters - compression without losing task performance.\n\nThis is the step we've been pointing toward: SOMA expanding from plain-text benchmarks to actual agent workloads.\n\nTimeline for the new challenge soon.",
      "created_at": "2026-04-17T12:00:19-03:00",
      "likes": 45,
      "retweets": 4,
      "replies": 0,
      "views": 791,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2044049994001309723",
      "handle": "SomaSubnet",
      "text": "RT @projectnobi_tao: \ud83c\udfc6 #SN114 SOMA Dashboard : Full Competition History is Live\n\nThe SOMA compression competition has been heating up acros\u2026",
      "created_at": "2026-04-14T10:47:49-03:00",
      "likes": 10,
      "retweets": 2,
      "replies": 0,
      "views": 829,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2043721002451165351",
      "handle": "SomaSubnet",
      "text": "\ud83d\udea8 We\u2019ve reduced our burn ratio to 50%\n\nThis helps us accelerate development, reward top-tier performance, and build a better product.\n\nWe now recognize winners across 5 categories, giving miners more ways to specialize where their algorithms perform best:\n\n\u27a1\ufe0f Overall Winner\n\u27a1\ufe0f Top Algorithm per ratio (0.2, 0.4, 0.6, 0.8)\n\nWe keep going.",
      "created_at": "2026-04-13T13:00:32-03:00",
      "likes": 37,
      "retweets": 2,
      "replies": 1,
      "views": 650,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2043043951918137597",
      "handle": "SomaSubnet",
      "text": "https://t.co/JHhYsPEo3U",
      "created_at": "2026-04-11T16:10:10-03:00",
      "likes": 50,
      "retweets": 8,
      "replies": 0,
      "views": 1542,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2041870664827564423",
      "handle": "SomaSubnet",
      "text": "Frontier models are the engine, but the CoT tax is a fuel leak. Seeing setups burn $200/day on reasoning tokens is exactly why we are building SOMA.\n\nWe\u2019re developing a compression layer to slash agentic costs without compromising on reasoning quality. Deep intelligence shouldn't be a luxury.",
      "created_at": "2026-04-08T10:27:57-03:00",
      "likes": 52,
      "retweets": 5,
      "replies": 1,
      "views": 989,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2040085808024265041",
      "handle": "SomaSubnet",
      "text": "Context compression is becoming essential for scalable #AI.\n\nAs agent workflows grow, context becomes noisier, slower to process, and more expensive to maintain.\n\nCompressing it can reduce token costs, speed up inference, and help improve output consistency.\n\n\u27a1\ufe0f Context compression may soon shift from a nice-to-have optimization to a core layer of #AI infrastructure.",
      "created_at": "2026-04-03T12:15:34-03:00",
      "likes": 43,
      "retweets": 3,
      "replies": 0,
      "views": 1004,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2038994785953013839",
      "handle": "SomaSubnet",
      "text": "\ud83c\udf10 We are introducing support for paraphrase-MiniLM-L3-v2 embeddings to SOMA.\n\nThis integration is designed to help SOMA capture semantic similarity during compression, moving beyond simple surface-level tokens.\n\nThe potential impact:\n\n\u27a1\ufe0f Smarter filtering: Ability to preserve core meaning rather than just keywords.\n\u27a1\ufe0f Better efficiency: The goal is to achieve higher compression quality within the same token budget.\n\nWe\u2019re excited to see how this semantic layer will evolve our context compression. More updates to come! \ud83d\ude80",
      "created_at": "2026-03-31T12:00:14-03:00",
      "likes": 43,
      "retweets": 7,
      "replies": 0,
      "views": 806,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2038921403374702676",
      "handle": "SomaSubnet",
      "text": "Sending raw, uncompressed context to an LLM is the new spaghetti code.\n\nIf the model is losing quality, it's because it's drowning in irrelevant data.\n\nBetter agents aren't built with bigger prompts, but with smarter ones.",
      "created_at": "2026-03-31T07:08:38-03:00",
      "likes": 33,
      "retweets": 3,
      "replies": 0,
      "views": 950,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2037567864035795111",
      "handle": "SomaSubnet",
      "text": "We benchmarked SOMA compression algorithm vs LLMLingua - SOTA prompt compression framework from Microsoft Research.\n\nThe results on LongBench v2, at 40% compression:\n\n\u27a1\ufe0f SOMA: 36.5\n\u27a1\ufe0f LLMLingua (xml): 33.5\n\u27a1\ufe0f LLMLingua (bert): 33.1\n\nSOMA consistently outperforms LLMLingua at the same compression level.\n\nWhat does 40% compression actually mean? Input tokens reduced to 40% of original size, which result in 60% fewer tokens and 60% lower inference cost.\n\nThis is just one benchmark, and we're testing SOMA across many more dimensions. But seeing consistent gains at this stage tells us we're on the right track.\n\nThe real goal isn\u2019t benchmarks though.\n\nIt\u2019s making #AI agents: cheaper, faster and without losing quality\n\nNext step: evaluating SOMA in real agent workflows.",
      "created_at": "2026-03-27T13:30:09-03:00",
      "likes": 50,
      "retweets": 8,
      "replies": 1,
      "views": 3021,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2037213334139715743",
      "handle": "SomaSubnet",
      "text": "RT @oli_soma: quick note on the @GoogleResearch TurboQuant drop\n\nthey're solving the physical bottleneck (3-bit KV cache). basically shrink\u2026",
      "created_at": "2026-03-26T14:01:23-03:00",
      "likes": 32,
      "retweets": 3,
      "replies": 2,
      "views": 1000,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2035416192622772385",
      "handle": "SomaSubnet",
      "text": "Most people working with #AI focus on models. The real bottleneck is context.\n\nEvery LLM system degrades as context grows:\n- higher latency\n- higher cost (token explosion)\n- lower signal-to-noise ratio\n- increasing hallucination risk\n\nWhy?\n\nBecause we treat context as append-only, not information-optimized.\n\n\ud83d\udc49 Compression fixes this.\n\nContext compression \u2260 naive summarization.\n\nIt\u2019s about:\n- preserving high-information-density tokens\n- removing redundancy\n- maintaining semantic integrity\n- optimizing for downstream reasoning\n\nThink of it as:\nan information bottleneck layer for LLM pipelines.\n\nInstead of feeding 10k tokens of loosely relevant history, you feed 1k tokens of maximally useful state.\n\nResult:\n- cheaper inference\n- faster responses\n- more stable agents over long horizons\n- better reasoning per token\n\nThis is exactly what is happening now on SOMA.\n\nSOMA compresses text while preserving key information structure - turning noisy context into high-signal input for models.\n\nNext step \u2192 compressing agent chain-of-thought: turning long reasoning traces into reusable, compact state.\n\nThis becomes critical for:\n- long-running agents\n- multi-step workflows\n- memory systems\n- tool-using LLMs\n\nTo get the most out of AI agents, we need the best possible context management.\n\nSOMA is that missing layer.\n\n\u27a1\ufe0f If you\u2019re building LLM systems and hitting scaling limits - let\u2019s talk.",
      "created_at": "2026-03-21T15:00:11-03:00",
      "likes": 46,
      "retweets": 5,
      "replies": 3,
      "views": 1341,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2035008531796304063",
      "handle": "SomaSubnet",
      "text": "\ud83e\uddea One subtle failure mode in LLM evaluation\n\nOpen-source models can look competitive on closed tasks, but degrade noticeably on open-ended QA - and part of that gap comes from the evaluation itself, not just model capability.\n\nL-Eval (https://t.co/l1cdcPBa4M) shows that standard metrics (F1, ROUGE) are strongly biased by answer length and style, which can distort model rankings.\n\nTheir proposed fix is LIE (Length-Instruction-Enhanced evaluation): enforcing answer length to reduce this bias and make comparisons more reliable.\n\n\ud83d\udcca As shown in the radar chart (attached), without length control, rankings vary across metrics. With LIE, they become much more consistent and better aligned with human judgment.\n\nIn addition to controlling length, we constrain the structure of answers using format hints (e.g., word, number) derived from the reference answer and applied during generation.\n\nImportantly, the scoring itself remains unchanged (exact match + token F1). The difference is that outputs are normalized before evaluation.\n\nWe interpret this as \"LIE-inspired normalization with added structural constraints\".\n\nThis leads to:\n-reduced variance from paraphrasing and style\n-more faithful comparison to ground truth\n-more stable evaluation signals, especially in compressed-context QA\n\n\u27a1\ufe0f In practice, the way you define output constraints determines what your metrics actually capture - because better validation drives better miners, which drives a stronger final product.",
      "created_at": "2026-03-20T12:00:17-03:00",
      "likes": 34,
      "retweets": 2,
      "replies": 0,
      "views": 803,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2034997430425370946",
      "handle": "SomaSubnet",
      "text": "Or\u2026 your context just isn\u2019t compressed enough.\n\nWith proper context compression, high token burn stops being a flex and starts looking like inefficiency.",
      "created_at": "2026-03-20T11:16:10-03:00",
      "likes": 53,
      "retweets": 3,
      "replies": 2,
      "views": 1421,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2034927796145823985",
      "handle": "SomaSubnet",
      "text": "RT @simplytao_: \ud83d\udea8 Templar (SN3) is available on the SimplyTao platform!\n\nHere you can easily buy #Bittensor subnet tokens with fiat. Choose\u2026",
      "created_at": "2026-03-20T06:39:28-03:00",
      "likes": 103,
      "retweets": 15,
      "replies": 2,
      "views": 4014,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2034664982277988648",
      "handle": "SomaSubnet",
      "text": "\ud83d\udea8 Round 1 of Context Compression is complete\n\nYou brought real algorithms and ideas. Every submission raised the bar for the entire community. Now we move forward together.\n\nRound 2 is ready. Refined scoring inspired by Length-Instruction-Enhanced methodology, stronger stability and security, and an upgraded dataset generation pipeline. Starts:\n\n\u27a1\ufe0f Mar 23, 14:00 UTC, submissions open\n\nThe goal remains the same. Build the best compression algorithm and bring it to the https://t.co/P1FYmt6bQX marketplace.\n\nWe are building a platform that delivers top tier algorithms and models to #AI agents through MCP, and the best submissions will be the first to power it.",
      "created_at": "2026-03-19T13:15:08-03:00",
      "likes": 49,
      "retweets": 6,
      "replies": 0,
      "views": 803,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    },
    {
      "tweet_id": "2033981807818063990",
      "handle": "SomaSubnet",
      "text": "There are 40 hours left in the evaluation window \ud83d\ude80\n\nA total of 210 miners participated in the upload phase of the first round of Context Compression.\n\nSoon we\u2019ll find out who becomes the first winner of the SOMA competition \ud83d\udc40 https://t.co/CqRUgvSjao",
      "created_at": "2026-03-17T16:00:27-03:00",
      "likes": 52,
      "retweets": 3,
      "replies": 0,
      "views": 910,
      "is_retweet": false,
      "is_reply": false,
      "media_type": null
    }
  ]
}