Baltor component updates
Reading the library updates feed…
- Harness components
- Distinct files
- Library changed
- This browser read it at
- Exact notice identity
Both downloads use the same notice displayed here, even if the library changes afterwards.
Agent Feeds
Sources your agents can check.
$9 a month. Free through December 31, 2026 (Eastern). No automatic charge; a paid subscription requires your explicit opt-in.
Pick the decision in front of your agent, such as choosing a coding model, and give it one link. Your agent reads the sources, what to compare and when to look again. Agent Feeds helps your agents decide; Harness Files helps them build, and Overnight / AFK Work keeps a local queue moving while you are away.
Your agents, your sources
Make a reading profile for each agent.
Choose sources for a coding assistant, a design researcher or any other agent. Download its JSON settings and reading guide, then add both files to that agent's working folder. Your harness owns scheduling and source access.
Customize and download a profile
Curated source collections
Start with the decision your agent needs to make.
19 collections with 60 source references. Each download includes a decision question, comparison fields, a review trigger and source-access notes. These are source directories, not live digests or current benchmark rankings.
Source documentation checked (UTC). Updates follow website releases; no daily refresh is promised.
Compare models
Choose a coding model
Compare coding evaluations before choosing a model and harness for repository work.
Sources: SWE-bench, Stanford HELM
Decision and review checklist
Which model and harness combination should handle this repository task?
When to revisit: Before changing the coding model or harness, or after a relevant evaluation or repository requirement changes.
Prepare a source-linked comparison for the same evaluation setting. Separate publisher-reported results from results we reproduced; leave incompatible settings side by side without a combined rank.
- benchmark version and split
- model and harness revision
- task population and exclusions
- resolved tasks and denominator
- cost and tool budget
Review RAG and retrieval quality
Choose what to measure when improving search, embeddings, reranking and grounded answers.
Decision and review checklist
Should we change the retriever, embeddings, reranker or answer-generation step?
When to revisit: Before redesigning retrieval, after a corpus or query change, and when observed answer quality declines.
Propose a retrieval comparison on a fixed corpus, then a separate answer-quality check. Explain which public benchmark resembles the task and which differences need a local test.
- corpus and query split
- embedding and reranker revision
- retrieval metric and cutoff
- answer metric and judge
- latency and failure population
Compare model cost and latency
Estimate the cost of a workload and inspect measured latency under stated conditions.
Sources: Artificial Analysis methodology, Anthropic API pricing
Decision and review checklist
Which provider and model meet the workload's quality, latency and spending constraints?
When to revisit: At initial architecture review, after pricing or traffic changes, and during scheduled cost reviews.
Make a cost worksheet for the supplied workload. Cite the provider's current price, show token and cache assumptions, and keep estimated spend separate from reported usage and observed invoices.
- provider and exact model
- input and output token basis
- cache and batch assumptions
- time to first token and throughput
- price date and measured versus estimated cost
Review model safety and reliability
Inspect a model's evaluated risks and the gaps that matter for a proposed use.
Sources: MLCommons AILuminate, Stanford HELM
Decision and review checklist
What evaluated risks remain for this model in the proposed use?
When to revisit: Before a new use case or model release, and when incidents or new evaluation evidence change the risk assessment.
Summarize the evaluated risks for the specified task with exact source references. Do not turn a score into a guarantee; list the missing conditions and an appropriate independent test.
- hazard and task scope
- benchmark version
- language and modality
- model and evaluator configuration
- failures and untested conditions
Run models on your hardware
Plan a model configuration that fits available memory and can be measured on the actual machine.
Sources: MLPerf Inference, llama.cpp benchmark tool, Ollama model library
Decision and review checklist
Which exact model configuration fits the machine and meets the required throughput?
When to revisit: Before choosing hardware or deploying a model, and after model, context, concurrency or runtime changes.
Separate memory estimates from measured compatibility. Propose a bounded benchmark using the exact hardware, model variant, context and runtime; do not claim a fit from parameter count alone.
- processor and accelerator
- RAM and accelerator memory
- model digest and quantization
- context and concurrency
- runtime revision and measured throughput
Track model prices and retirements
Catch price changes, new models and retirement dates before they force a rushed model switch.
Sources: Anthropic API pricing, Claude models overview, Claude model deprecations, OpenAI API pricing, OpenAI API deprecations, Gemini API pricing, Gemini API deprecations, OpenRouter models API, Artificial Analysis methodology
Decision and review checklist
Should we keep, re-price or replace the current model after a price change, release or retirement notice?
When to revisit: When a provider announces a price change, model release or retirement, and before each budget review.
Check the named providers for price changes, new models and retirement notices that affect the current configuration. Cite each provider page with the date it was read, keep listed prices separate from observed invoices, and propose a side-by-side test before any switch.
- provider, model identifier and platform
- price basis and effective date
- announced retirement or shutdown date
- recommended replacement and its limits
- quality and cost measured on our own test set
Build and operate
Assess a model release
Find model changes relevant to a task, then check access, licence and integration requirements.
Sources: Hugging Face Hub API, Ollama model library
Decision and review checklist
Does a new model offer enough relevant benefit to justify an integration test?
When to revisit: When a chosen publisher releases a model or changes access, licence, support or deployment requirements.
Review the chosen publishers for relevant releases. Link the model card and exact revision, distinguish author claims from independent measurements, and identify a small compatibility test.
- publisher and repository
- exact revision or digest
- release and observation dates
- licence and gated access
- supported task and deployment method
Choose MCP tools and services
Discover services an agent can connect to and inspect permissions before enabling them.
Sources: Official MCP Registry, GitHub Releases API
Decision and review checklist
Which service supplies the required capability with acceptable permissions and maintenance?
When to revisit: Before enabling a server, after a protocol or authentication change, and during periodic access reviews.
Build a shortlist for the named task. Cite each server's official documentation, show required access and possible effects, and flag breaking changes without installing or connecting automatically.
- publisher and server identity
- protocol and server version
- transport and authentication
- requested scopes and effects
- source licence and maintenance
Turn papers into practical tests
Find research that may improve a specific workflow and turn its claims into a reproducible test plan.
Sources: arXiv API and feeds, OpenAlex
Decision and review checklist
Which published method could improve this problem, and what would establish that it works here?
When to revisit: During initial design, when a measured bottleneck appears, and when new evidence challenges the chosen method.
Find papers for the supplied problem and explain the practical method in original words. Link the exact version, note conflicting evidence and outline a test before recommending an implementation.
- paper identifier and version
- publication versus update date
- claimed method and baseline
- code and data availability
- licence and reproduction status
Pick a founder stack
Compare a small operating stack against a startup's workload, budget and migration needs.
Sources: Cloudflare Workers, Cloudflare R2, Fly.io, Resend, Better Auth
Decision and review checklist
Which email, login, compute and storage choices fit the product and its operating budget?
When to revisit: At initial design and during reviews of cost, reliability, customer requirements or migration needs.
Compare two workable stacks for the supplied constraints. Assign one owner to login, email, compute and storage; cite current limits and explain what remains custom. Do not create accounts or spend money.
- job and deployment constraints
- current limits and billing units
- identity and secret ownership
- backup and export path
- failure and migration procedure
Plan platform and tool upgrades
Find relevant releases, breaking changes and deprecations for a chosen technology stack.
Sources: GitHub Releases API, GitHub Changelog, Cloudflare Changelog
Decision and review checklist
Which announced changes require action in this stack, and which can wait?
When to revisit: When a tracked dependency releases or deprecates a feature, and before a scheduled maintenance window.
Review only the named dependencies and services. Separate a release announcement from a verified installation, and produce a change list with source links, applicability and a test or rollback step.
- publisher and repository
- version or announcement identity
- published and effective dates
- affected feature or dependency
- migration and rollback steps
Upgrade an agent framework
Find breaking changes in agent frameworks, SDKs and the MCP specification before upgrading a working system.
Sources: MCP specification changelog, MCP Python SDK releases, MCP TypeScript SDK releases, OpenAI Agents SDK releases, Claude Agent SDK for Python releases, LangGraph releases, Pydantic AI releases, GitHub Releases API
Decision and review checklist
Is this framework or SDK upgrade worth an integration test now, or can it wait?
When to revisit: When a pinned framework, SDK or MCP revision publishes a release, and before you update dependencies.
Compare the pinned version with the target release for each named package. Link the relevant release notes, separate stable releases from pre-releases, and list the integration tests to run before upgrading. Do not install or upgrade anything.
- package, repository and pinned version
- target release and publication date
- breaking changes and deprecations
- protocol revision supported
- integration test result and rollback version
Standardize a coding agent version
Choose which coding agent and version a team should run, and see what changed since the pinned one.
Sources: Claude Code changelog, OpenAI Codex CLI releases, OpenCode releases, Gemini CLI release notes, Cursor changelog, Aider release history
Decision and review checklist
Which harness and version should the team standardize on, and what changed since the pinned one?
When to revisit: When a harness the team uses publishes a stable release, and before changing the team's standard setup.
Summarize changes between the team's pinned version and the newest stable release of each named harness. Flag changes to defaults, permission prompts, sandboxing and supported models, then propose a short trial on a real repository task before standardizing.
- harness and pinned version
- release channel and date
- changed defaults, permissions and tools
- supported models and providers
- licence and data terms
Check dependencies for advisories
Find published vulnerabilities that affect the packages, tools and MCP servers an agent setup depends on.
Sources: OSV API, GitHub global security advisories API, CISA Known Exploited Vulnerabilities catalog
Decision and review checklist
Does a published advisory affect a dependency we run, and how soon must we act?
When to revisit: Before a release, after adding a dependency or MCP server, and when an advisory source publishes a matching entry.
Check the supplied dependency list against the advisory sources. Report exact matches with advisory links and fixed versions, mark uncertain matches, and propose the smallest safe update with a test. Do not change dependencies.
- package, ecosystem and installed version
- advisory identifier and aliases
- affected and fixed version ranges
- evidence of known exploitation
- fix, mitigation and verification step
Creative and 3D
Choose a 3D or game engine
Compare engines and 3D tools a creative agent can build on, using their own documentation and release notes.
Sources: Godot Engine documentation, Godot download archive, Blender release notes, Babylon.js documentation, Babylon.js releases
Decision and review checklist
Which engine or tool should a creative agent build this project on?
When to revisit: At project start, before a major engine upgrade, and when a required platform or export format changes.
Compare the named engines for the supplied project. Cite official documentation for scripting, export and platform support, note version-specific limits, and propose a small prototype that tests the riskiest requirement. Do not install or download anything.
- project type and target platforms
- engine version and release channel
- scripting and automation interface
- import, export and asset pipeline
- licence for engine, scripts and assets
Connect creative tools through MCP
Decide which MCP connection to a 3D tool is acceptable to enable, and under what isolation.
Sources: Blender Lab MCP Server, MCP for Blender (community project), Godot MCP (community project), MCP security best practices, Official MCP Registry
Decision and review checklist
Which MCP connection to this creative tool is acceptable to enable, and with what isolation?
When to revisit: Before enabling a server, after a server or protocol update, and before the project handles private files.
Compare the named servers for the creative task. Show which tools can run code, write files or reach external services, and recommend an isolated setup such as a virtual machine. Do not install, connect or pre-approve tools.
- publisher and official or community status
- code execution and file access
- external services and telemetry
- maintenance activity and supported versions
- isolation required before first use
Find opportunities
Track competitors and useful tools
Find products that solve a named problem, then verify the claims at their own documentation.
Sources: Product Hunt API documentation, GitHub Changelog
Decision and review checklist
Should we build, buy or reposition after comparing products that address this customer problem?
When to revisit: During product positioning, before building a major feature, and when a relevant competitor changes its offering.
Prepare a problem-based shortlist with primary product links and explicit unknowns. Do not equate launch popularity with usefulness or collect Product Hunt commercially without recorded permission.
- problem and target user
- publisher's product documentation
- access and pricing date
- integration and export options
- measured versus advertised benefit
Find a fitting hackathon
Find events that match a project and check whether a reusable public demonstration is allowed.
Sources: Official Kaggle CLI, Devpost hackathons
Decision and review checklist
Which event fits our project, eligibility and ability to publish the intended demonstration?
When to revisit: Before committing project time, and again when event rules or submission deadlines change.
Shortlist events for the stated skills, location and schedule. Verify each deadline and rule on the organizer's page; distinguish an eligible opportunity from a submitted application.
- organizer and event URL
- deadline and timezone
- eligibility and team rules
- data and code sharing rules
- submission requirements
Research market explanations
Research public filings and economic releases that may explain a market event.
Sources: SEC EDGAR APIs, Federal Reserve Bank of St. Louis FRED API
Decision and review checklist
Which public events could explain the observed market activity, and which explanations remain unsupported?
When to revisit: After a supplied market event or relevant filing or economic release, with event time and data vintage preserved.
Build a dated source trail for the supplied event. Keep alternative explanations and missing market data visible. Do not infer trader identity, confirmed rolls or profitable signals from public context alone.
- issuer or economic series
- event and publication timestamps
- filing amendment or data vintage
- units and adjustment basis
- observed fact versus explanation
Source links do not authorize copying, installing, connecting accounts or sending messages. Review each provider's terms and each item's licence. Product Hunt commercial API ingestion requires its permission and is not enabled here.
Set up
Give each agent only the feeds it needs
Public feeds need no account and no key. Pick one to three collections for each agent, put its Markdown or JSON Feed link in the agent's instructions file, and let your harness read it once a day or before each decision. 19 collections, five steps.
Download a separate reading profile for each agent. Its files record the sources you selected and the reading interval for your harness to configure.
Library updates feed
Read an actual feed item.
This notice comes from the library Baltor serves now. It reports what the library holds, not current model news or benchmark results.
For an agent that polls for changes, use these stable feed addresses:
This is one current-state notice in three formats, not a complete change history. A failed refresh does not mean the library is empty. Markdown here is plain text for agents, not a claim of Open Knowledge Format compatibility.
Research-decision specimen · curated example
Where should a research-feed product run?
This example shows how an agent could use four of the source collections below. It is a decision brief, not an automated investigation or a measured Baltor cost comparison.
The decision
Choose where to serve public feeds, store artifacts and run a custom search index.
Example assumptions: Public reads are frequent; source collection is periodic; the search index uses a custom Python process and persistent local files. These are scenario inputs, not measured traffic.
| Evidence | What the source says | What is not measured here |
|---|---|---|
| Publisher documentation | Source statementWorkers limits define the runtime and resource constraints a collection job must fit. | Not measured hereWhether this job fits those limits; its failure rate and end-to-end latency. |
| Publisher documentation | Source statementR2 pricing separates storage and operation charges from egress. | Not measured hereActual requests, storage mix and monthly bill. No savings percentage is established. |
| Publisher documentation | Source statementFly Machines provide virtual machines for application processes. | Not measured hereSearch recall, index rebuild time, operating effort and cost for the same workload. |
Provisional choice: Test Cloudflare for anonymous feed delivery and artifact storage. Keep the custom search process on its existing host until an alternative passes the same workload and recovery checks. This is an inference from the scenario and documentation, not a deployment instruction.
Evidence that could change the choice
- A fixed workload with actual request counts, storage operations, observed latency and a complete cost estimate.
- The same search queries and relevance judgments on each engine, including failures and index rebuild time.
- A restore and rollback test, with identity and access checks unchanged.
Review trigger: Revisit when traffic, provider pricing, runtime limits or the required search behavior changes. An agent must collect new evidence before changing the decision; this page does not schedule that work.
Documentation checked 2026-10-08 (UTC). Workload measurements: not collected for this specimen.
Collections that support this decision
- Pick a founder stack: Compare a small operating stack against a startup's workload, budget and migration needs.
- Compare model cost and latency: Estimate the cost of a workload and inspect measured latency under stated conditions.
- Review RAG and retrieval quality: Choose what to measure when improving search, embeddings, reranking and grounded answers.
- Plan platform and tool upgrades: Find relevant releases, breaking changes and deprecations for a chosen technology stack.
Agent Feeds supports the choice and its reasons. Harness Files supplies the parsers, comparison scripts, deployment files and checks used to carry it out.
Harness Files
Agent Feeds supplies public source collections and the library updates feed. Agent Feeds + Harness Files also gives you the whole library: skills, tools, code, hooks and settings your harness downloads at an exact version. The full-library plan is $49 a month.
A feed notice never installs or runs its contents. Downloaded files still require their normal authorization, source and licence checks. The Public Good collection is free with an account.
Overnight / AFK Work · Preview
Run local task queues with call limits, task checkpoints and morning reports. Your worker and model access stay under your control.
No extra charge with Harness Files. The local tools are free and open source.