{
  "model": "gpt-4o-mini",
  "messages": [
    {
      "role": "system",
      "content": "You classify technical content into a fixed vocabulary of topic labels.\nYour output MUST be valid JSON with shape:\n  { \"classifications\": [ { \"id\": \"<candidate-id>\", \"topics\": [\"label1\", \"label2\"] }, ... ] }\nEach candidate gets 1-3 topics from the canonical vocabulary.\nIf a candidate does not fit any vocabulary label, return an empty topics array for it.\nDo not invent labels outside the vocabulary.\n\nCanonical vocabulary:\n  - frontier-models\n  - agent-evals\n  - agent-orchestration\n  - agent-memory\n  - tool-calling\n  - rag\n  - fine-tuning\n  - prompt-engineering\n  - model-routing\n  - mcp-spec\n  - opentelemetry\n  - agent-observability\n  - telemetry-pipelines\n  - multi-tenant-observability\n  - schema-evolution\n  - data-lineage\n  - ai-cost-control\n  - ai-safety\n  - ai-governance\n  - human-in-the-loop"
    },
    {
      "role": "user",
      "content": "Classify each candidate below into 1-3 topics from the canonical vocabulary.\nReturn a single JSON object matching the schema in the system prompt.\n\nCandidates:\n---\nid: rss::huggingface-blog/https://huggingface.co/blog/allenai/benchmirt\ntype: rss\nsource: huggingface-blog\ntitle: BenchMIRT: What are LLM benchmarks actually measuring?\ncontent: \n---\nid: rss::huggingface-blog/https://huggingface.co/blog/webgpu-kernels\ntype: rss\nsource: huggingface-blog\ntitle: Introducing @huggingface/kernels: 200+ WebGPU Kernels for Local AI\ncontent: \n---\nid: rss::openai-blog/https://openai.com/index/ai-native-company-workflows\ntype: rss\nsource: openai-blog\ntitle: How AI-native companies turn workflows into operating capability\ncontent: Basis, Clay, and Exa Labs use AI agents to improve onboarding, account management, and developer integrations. See what enterprise leaders can apply.\n---\nid: rss::openai-blog/https://openai.com/index/path-to-astra\ntype: rss\nsource: openai-blog\ntitle: Path to ...[truncated]

{
  "id": "chatcmpl-EJRDJtHRu1EAQJblsQnm7nTajl7u0",
  "object": "chat.completion",
  "created": 1788301549,
  "model": "gpt-4o-mini-2024-07-18",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "{\n  \"classifications\": [\n    {\n      \"id\": \"rss::huggingface-blog/https://huggingface.co/blog/allenai/benchmirt\",\n      \"topics\": [\"agent-evals\"]\n    },\n    {\n      \"id\": \"rss::huggingface-blog/https://huggingface.co/blog/webgpu-kernels\",\n      \"topics\": []\n    },\n    {\n      \"id\": \"rss::openai-blog/https://openai.com/index/ai-native-company-workflows\",\n      \"topics\": [\"agent-orchestration\"]\n    },\n    {\n      \"id\": \"rss::openai-blog/https://openai.com/index/path-to-astra\",\n      \"topics\": [\"frontier-models\", \"ai-safety\"]\n    },\n    {\n      \"id\": \"rss::openai-blog/https://openai.com/index/chatgpt-connects-health-records-and-healthcare-sources\",\n      \"topics\": [\"tool-calling\"]\n    },\n    {\n      \"id\": \"rss::openai-blog/https://openai.com/index/supporting-california-bill-advance-ai-youth-safety\",\n      \"topics\": [\"ai-governance\", \"ai-safety\"]\n    },\n    {\n      \"id\": \"rss::aws-machine-learning/fe5a761d13fdea1cd6343d646c1e042d79f66815\",\n      \"topics\": [\"frontier-models\"]\n    },\n    {\n      \"id\": \"rss::aws-machine-learning/c09782028dd4cf40d9245e4cd28716a51318e5b0\",\n      \"topics\": [\"agent-orchestration\"]\n    },\n    {\n      \"id\": \"rss::aws-machine-learning/3e0afa841f894aea05b2ebf4260dd9e6fe0d4ab0\",\n      \"topics\": [\"ai-cost-control\"]\n    },\n    {\n      \"id\": \"rss::aws-machine-learning/008129bbc0eabcb083993ebf3af85f5167177c99\",\n      \"topics\": [\"agent-observability\"]\n    },\n    {\n      \"id\": \"rss::aws-machine-learning/6d27453be64ca6b4b4bfe0edebc3b8aa1367edf7\",\n      \"topics\": [\"agent-orchestration\"]\n    },\n    {\n      \"id\": \"rss::aws-machine-learning/a64230827a5326f30ac219e986bfd033...[truncated]

Source: huggingface-blog
Title: BenchMIRT: What are LLM benchmarks actually measuring?
Content: 

{"score": 1, "topic": "BenchMIRT: What are LLM benchmarks actually measuring?", "reason": "The content is likely a discussion or analysis of benchmarks, which doesn't directly relate to recent commits or development activities in the repos.", "voiceMode": "uday"}

Candidate id: rss::openai-blog/https://openai.com/index/ai-native-company-workflows
Type: rss
Source: openai-blog
Title: How AI-native companies turn workflows into operating capability
Topics: agent-orchestration
Content excerpt: Basis, Clay, and Exa Labs use AI agents to improve onboarding, account management, and developer integrations. See what enterprise leaders can apply.

Score: 1/10
Score reason: The content is about other companies using AI agents, which doesn't directly relate to recent commits or activities in the developer's projects.

Topic memory snapshot:
  - agent-orchestration: seen=416, posted=7, fatigue=high, recently_posted=true

TREND CLUSTER (this candidate is part of):
  Topic: agent-orchestration
  Sources surfacing in last 72h: 3
  First seen: 2026-09-01T15:50:00.000Z
  Last seen: 2026-09-01T17:00:00.000Z

{
  "worthPosting": false,
  "reason": "The content focuses on other companies' use of AI agents, which doesn't align with the recent high fatigue on agent-orchestration topics.",
  "isTrend": false,
  "trendSummary": "",
  "angle": "",
  "mentionGman": false,
  "tone": "",
  "draftHints": "",
  "topicFatigueNote": "High fatigue on agent-orchestration content, and this post does not introduce new insights."
}

{
  "model": "gpt-4o-mini",
  "messages": [
    {
      "role": "system",
      "content": "You classify technical content into a fixed vocabulary of topic labels.\nYour output MUST be valid JSON with shape:\n  { \"classifications\": [ { \"id\": \"<candidate-id>\", \"topics\": [\"label1\", \"label2\"] }, ... ] }\nEach candidate gets 1-3 topics from the canonical vocabulary.\nIf a candidate does not fit any vocabulary label, return an empty topics array for it.\nDo not invent labels outside the vocabulary.\n\nCanonical vocabulary:\n  - frontier-models\n  - agent-evals\n  - agent-orchestration\n  - agent-memory\n  - tool-calling\n  - rag\n  - fine-tuning\n  - prompt-engineering\n  - model-routing\n  - mcp-spec\n  - opentelemetry\n  - agent-observability\n  - telemetry-pipelines\n  - multi-tenant-observability\n  - schema-evolution\n  - data-lineage\n  - ai-cost-control\n  - ai-safety\n  - ai-governance\n  - human-in-the-loop"
    },
    {
      "role": "user",
      "content": "Classify each candidate below into 1-3 topics from the canonical vocabulary.\nReturn a single JSON object matching the schema in the system prompt.\n\nCandidates:\n---\nid: rss::openai-blog/https://openai.com/index/polimill\ntype: rss\nsource: openai-blog\ntitle: Polimill builds Japan's next-generation public AI infrastructure\ncontent: Polimill uses OpenAI GPT models and Codex to help municipalities search and use administrative knowledge while accelerating development.\n---\nid: rss::openai-blog/https://openai.com/index/expanding-access-to-ai-with-chatgpt-ads\ntype: rss\nsource: openai-blog\ntitle: A milestone in expanding access to AI\ncontent: ChatGPT Ads reaches $1 billion in annualized revenue run rate and expands globally, supporting broader access to AI through free and affordable options.\n---\nid: rss::aws-machine-learning/0b043ce49f9a164fa994a19b2ff7903218809861\ntype: rss\nsource: aws-machine-learning\ntitle: Connect an AgentCore Runtime hosted MCP server to Amazon Quick\ncontent: I...[truncated]

{
  "id": "chatcmpl-EJ5seRUT2c3oNM3SDzWdpSKlXVvWC",
  "object": "chat.completion",
  "created": 1788219544,
  "model": "gpt-4o-mini-2024-07-18",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "{\n  \"classifications\": [\n    {\n      \"id\": \"rss::openai-blog/https://openai.com/index/polimill\",\n      \"topics\": [\"agent-orchestration\"]\n    },\n    {\n      \"id\": \"rss::openai-blog/https://openai.com/index/expanding-access-to-ai-with-chatgpt-ads\",\n      \"topics\": []\n    },\n    {\n      \"id\": \"rss::aws-machine-learning/0b043ce49f9a164fa994a19b2ff7903218809861\",\n      \"topics\": [\"mcp-spec\", \"agent-orchestration\"]\n    },\n    {\n      \"id\": \"rss::aws-machine-learning/4f41b92df4986db71222a85598ec0047876fc027\",\n      \"topics\": []\n    },\n    {\n      \"id\": \"rss::aws-machine-learning/a35b8e6267585714982a44274684919dd8bb62e5\",\n      \"topics\": [\"agent-registry\"]\n    },\n    {\n      \"id\": \"rss::aws-machine-learning/323478b1ec9cbb8e9bd70aeb19b445443b64f0e8\",\n      \"topics\": [\"agent-observability\", \"model-routing\"]\n    },\n    {\n      \"id\": \"rss::aws-machine-learning/aa6871124c0907be369777e6369d35b6c44e2000\",\n      \"topics\": [\"multi-tenant-observability\", \"agent-orchestration\"]\n    },\n    {\n      \"id\": \"rss::cloudflare-blog/01M15GV7R8AFPM9KDM9HXDKJZ4\",\n      \"topics\": [\"ai-safety\"]\n    },\n    {\n      \"id\": \"rss::vercel-blog/https://vercel.com/changelog/fx-ai-sdk-harness-adapter\",\n      \"topics\": [\"tool-calling\"]\n    },\n    {\n      \"id\": \"rss::vercel-blog/https://vercel.com/changelog/set-per-user-budgets-on-ai-gateway\",\n      \"topics\": [\"ai-cost-control\"]\n    },\n    {\n      \"id\": \"rss::vercel-blog/https://vercel.com/blog/how-our-agents-build-on-brand-pages-with-design-md\",\n      \"topics\": [\"agent-orchestration\"]\n    },\n    {\n      \"id\": \"rss::vercel-blog/https://vercel.com/changelog/vercel-sandbox-n...[truncated]

Source: openai-blog
Title: Polimill builds Japan's next-generation public AI infrastructure
Content: Polimill uses OpenAI GPT models and Codex to help municipalities search and use administrative knowledge while accelerating development.

{"score": 1, "topic": "Polimill uses OpenAI models for Japan's AI infrastructure", "reason": "The content is about a third-party application of OpenAI models and not directly related to the developer's projects or recent commits.", "voiceMode": "uday"}

Candidate id: rss::openai-blog/https://openai.com/index/polimill
Type: rss
Source: openai-blog
Title: Polimill builds Japan's next-generation public AI infrastructure
Topics: agent-orchestration
Content excerpt: Polimill uses OpenAI GPT models and Codex to help municipalities search and use administrative knowledge while accelerating development.

Score: 1/10
Score reason: The content is about a third-party application of OpenAI models and not directly related to the developer's projects or recent commits.

Topic memory snapshot:
  - agent-orchestration: seen=408, posted=7, fatigue=high, recently_posted=true

TREND CLUSTER (this candidate is part of):
  Topic: agent-orchestration
  Sources surfacing in last 72h: 3
  First seen: 2026-08-31T04:00:00.000Z
  Last seen: 2026-08-31T22:47:53.000Z

{
  "worthPosting": false,
  "reason": "The content focuses on a third-party application of OpenAI models, which does not align with the core themes of AI infrastructure and observability.",
  "isTrend": false,
  "trendSummary": "",
  "angle": "",
  "mentionGman": false,
  "tone": "",
  "draftHints": "",
  "topicFatigueNote": "High fatigue on agent-orchestration content, with recent posts already covering similar themes."
}

{
  "model": "gpt-4o",
  "messages": [
    {
      "role": "system",
      "content": "\nYou write tweets as Uday Kumar (@gman_ai), a solo engineer building GMAN\n(observability for AI agents), automated trading systems, and a real-time\nmarket data warehouse.\n\n\nCompany: GMAN (GetMyAgentNow)\nProduct: Behavioral evidence system for AI-agent operations — records observed agent activity and shows what the evidence can and cannot establish.\nHow it works: The published @gman-ai/dev SDK is the earlier proxy-era developer preview: it runs a local proxy, and requests routed through it are recorded — prompts, responses, tokens, latency, cost — in the web dashboard (cockpit) at getmyagentnow.com. A behavioral recorder for Claude Code sessions is built and not yet released.\nStage: Developer preview, SDK v0.1.9 on npm, live dashboard at getmyagentnow.com/demo.\nBuilder: Uday Kumar. Solo engineer. Also builds automated trading systems (gex-advisor, paisamaker, market-data-fetcher).\nBio: \"Uday Kumar | Building GMAN — observability for AI agents. This account is partly run by an AI agent. Watch it think → getmyagentnow.com/demo\"\n\n\nYou are reacting to something ANOTHER company or team published — an\nannouncement, release, or piece of research. NOT your own work. Your job is to\nadd a practitioner's TAKE, not to report the news.\n\nTHE ONE RULE:\nNever lead with \"X announced Y.\" Lead with what it MEANS, what it confirms, or\nwhat people building agents should take from it. The announcement is evidence\nfor your point, not the headline.\n\n  Bad:  \"AWS added silent-failure detection to Bedrock.\"\n  Good: \"Silent-failure detection shipping into Bedrock is the tell: agent\n         observability is table stakes now, not a nice-to-have.\"\n\nFORMAT MODE — pick one:\n\nMode A (The tell): read the move as a signal about where the field is going.\n  \"X shipping Y means the industry is converging on <belief>.\"\n\nMode B (Practitioner's angle): why it matters to someone r...[truncated]

Centralized agent registries like AWS Agent Registry are crucial for scaling AI operations. They streamline agent observability and management, reducing chaos in production environments. If you're running agents in prod, this setup helps ensure consistency and efficiency. #AgentEvals #ObservabilityAI

{
  "model": "gpt-4o-mini",
  "messages": [
    {
      "role": "system",
      "content": "You classify technical content into a fixed vocabulary of topic labels.\nYour output MUST be valid JSON with shape:\n  { \"classifications\": [ { \"id\": \"<candidate-id>\", \"topics\": [\"label1\", \"label2\"] }, ... ] }\nEach candidate gets 1-3 topics from the canonical vocabulary.\nIf a candidate does not fit any vocabulary label, return an empty topics array for it.\nDo not invent labels outside the vocabulary.\n\nCanonical vocabulary:\n  - frontier-models\n  - agent-evals\n  - agent-orchestration\n  - agent-memory\n  - tool-calling\n  - rag\n  - fine-tuning\n  - prompt-engineering\n  - model-routing\n  - mcp-spec\n  - opentelemetry\n  - agent-observability\n  - telemetry-pipelines\n  - multi-tenant-observability\n  - schema-evolution\n  - data-lineage\n  - ai-cost-control\n  - ai-safety\n  - ai-governance\n  - human-in-the-loop"
    },
    {
      "role": "user",
      "content": "Classify each candidate below into 1-3 topics from the canonical vocabulary.\nReturn a single JSON object matching the schema in the system prompt.\n\nCandidates:\n---\nid: rss::openai-blog/https://openai.com/index/expanding-access-to-ai-with-chatgpt-ads\ntype: rss\nsource: openai-blog\ntitle: A milestone in expanding access to AI\ncontent: ChatGPT Ads reaches $1 billion in annualized revenue run rate and expands globally, supporting broader access to AI through free and affordable options.\n---\nid: rss::aws-machine-learning/ac351c6a258d9c6b09e4e26e7198133fc26a60a3\ntype: rss\nsource: aws-machine-learning\ntitle: Batch write and discover records in Amazon SageMaker Feature Store\ncontent: Amazon SageMaker Feature Store now supports two new APIs: BatchWriteRecord writes up to 25 records across multiple feature groups in a single call, and ListRecords enumerates record identifiers within a feature group. In this post, we walk through each API with code examples you can use to get started.\n---\n...[truncated]

{"classifications":[{"id":"rss::openai-blog/https://openai.com/index/expanding-access-to-ai-with-chatgpt-ads","status":"no-canonical-match (filtered)"},{"id":"rss::aws-machine-learning/ac351c6a258d9c6b09e4e26e7198133fc26a60a3","topics":["rag"]},{"id":"rss::cloudflare-blog/01M15GV7R8AFPM9KDM9HXDKJZ4","status":"no-canonical-match (filtered)"},{"id":"rss::vercel-blog/https://vercel.com/changelog/minimax-h3-and-h3-max-are-50-off-on-ai-gateway","status":"no-canonical-match (filtered)"},{"id":"rss::simon-willison/https://simonwillison.net/2026/Aug/30/understanding-chatgpt-work/","status":"no-canonical-match (filtered)"},{"id":"rss::simon-willison/https://simonwillison.net/2026/Aug/29/hy4/","status":"no-canonical-match (filtered)"},{"id":"rss::simon-willison/https://simonwillison.net/2026/Aug/28/just-a-rumour-of-a-bug/","status":"no-canonical-match (filtered)"},{"id":"rss::latent-space/https://www.latent.space/p/ainews-openai-shuts-off-cursor","status":"no-canonical-match (filtered)"},{"id":"rss::datadog-blog/https://www.datadoghq.com/blog/bitbucket-cloud-source-code-integration/","topics":["agent-observability"]},{"id":"rss::opentelemetry-blog/https://opentelemetry.io/blog/2026/go-logs-api-sdk-rc/","topics":["opentelemetry"]},{"id":"rss::honeycomb-blog/https://www.honeycomb.io/blog/fin-cto-building-great-engineering-organizations-ai-era","topics":["agent-observability"]},{"id":"rss::aws-big-data/092741ff8ccf9ff0fea9ea4810f51e835a692a01","topics":["data-lineage"]},{"id":"rss::aws-big-data/9b304b56606038dfa42fe23fe7695c503b9144ba","topics":["telemetry-pipelines"]}]}

Source: aws-machine-learning
Title: Batch write and discover records in Amazon SageMaker Feature Store
Content: Amazon SageMaker Feature Store now supports two new APIs: BatchWriteRecord writes up to 25 records across multiple feature groups in a single call, and ListRecords enumerates record identifiers within a feature group. In this post, we walk through each API with code examples you can use to get started.

{"score": 5, "topic": "New APIs for batch writing and discovering records in SageMaker Feature Store", "reason": "The introduction of new APIs for batch operations and record discovery in SageMaker Feature Store closes a capability gap, but doesn't tell a compelling story beyond the feature addition.", "voiceMode": "uday"}

{
  "model": "gpt-4o-mini",
  "messages": [
    {
      "role": "system",
      "content": "You are a senior technical analyst examining one day's multi-source signal\nobservations. Your job is to decide if there is a SYNTHESIS post worth\ndrafting — a single tweet that names a pattern, theme, or connection across\nMULTIPLE INDEPENDENT observations that NO individual observation explicitly\nstated on its own.\n\nSynthesis vs summary:\n- SUMMARY: 'OpenAI shipped X this week.' (one source, paraphrasing)\n- SYNTHESIS: 'OpenAI, Anthropic, and Google all moved to structured tool\n  execution this month — the convergence point is X.' (multiple sources,\n  cross-source claim, says something none of them said alone.)\n\nHard rules:\n- worthDrafting=true REQUIRES observations_cited to reference >=2 candidates\n  from DIFFERENT sources.\n- worthDrafting=true REQUIRES a claim that no individual observation made\n  explicitly.\n- If you find yourself summarizing one article or restating one observation,\n  return worthDrafting=false.\n- Most runs SHOULD produce worthDrafting=false. Synthesis-worthy patterns\n  are rare. Do NOT manufacture one. False is a correct answer.\n- confidence is your honest read 0-1. <0.5 means weak — force\n  worthDrafting=false in that case.\n\nOutput JSON (strict):\n{\n  \"worthDrafting\": boolean,\n  \"theme\": \"short label, e.g. \\\"tool-execution-convergence\\\"\",\n  \"observations_cited\": [\"candidate_id1\", \"candidate_id2\"],\n  \"claim\": \"the specific cross-source claim in plain language\",\n  \"reasoning\": \"why these observations together support this claim\",\n  \"confidence\": number between 0 and 1\n}\n\nIf no synthesis is genuine: return worthDrafting=false with a brief\nreasoning explaining why nothing rose above noise (e.g., 'two near-trends\nbut no shared mechanism', 'all observations are independent moves')."
    },
    {
      "role": "user",
      "content": "## Observations from this cycle\n\nCandidates with topics:\n  - id=...[truncated]

{
  "worthDrafting": false,
  "reasoning": "There are no clear patterns or themes emerging from the observations. The near-trends identified do not have sufficient convergence across multiple sources to support a synthesis claim.",
  "confidence": 0.8
}