Anthropic just introduced Claude Fable 5 and Claude Mythos 5 π
The interesting part:
Fable 5 and Mythos 5 are basically the same underlying model, but with different access and safety layers.
β Claude Fable 5 is the version available for general users. Anthropic says it is their most capable generally available model so far, especially for:
β’ Software Engineering
β’ Long-running agentic tasks
β’ Vision-based work
β’ Knowledge work
β’ Scientific reasoning
β’ Long-context workflows
One example they shared is pretty wild:
Stripe tested it on a 50M-line Ruby codebase, and it completed a migration in one day that would normally take a team more than two months. π€―
But because the model is much stronger, Anthropic is adding stricter safeguards.
For risky areas like cybersecurity, biology/chemistry, and model distillation, some requests will automatically fall back to Claude Opus 4.8 instead of Fable 5. β οΈ
Claude Mythos 5 is the restricted version, mainly for trusted Cyber defenders, infrastructure providers, and later selected biology researchers. It has some safeguards lifted depending on the access program.
Pricing is πΈ:
β’ $10 / 1M input tokens
β’ $50 / 1M output tokens
[ Read More ] : https://www.anthropic.com/news/claude-fable-5-mythos-5
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#AI #Claude #LLM
@ProgrammingTip
The interesting part:
Fable 5 and Mythos 5 are basically the same underlying model, but with different access and safety layers.
β Claude Fable 5 is the version available for general users. Anthropic says it is their most capable generally available model so far, especially for:
β’ Software Engineering
β’ Long-running agentic tasks
β’ Vision-based work
β’ Knowledge work
β’ Scientific reasoning
β’ Long-context workflows
One example they shared is pretty wild:
Stripe tested it on a 50M-line Ruby codebase, and it completed a migration in one day that would normally take a team more than two months. π€―
But because the model is much stronger, Anthropic is adding stricter safeguards.
For risky areas like cybersecurity, biology/chemistry, and model distillation, some requests will automatically fall back to Claude Opus 4.8 instead of Fable 5. β οΈ
Claude Mythos 5 is the restricted version, mainly for trusted Cyber defenders, infrastructure providers, and later selected biology researchers. It has some safeguards lifted depending on the access program.
Pricing is πΈ:
β’ $10 / 1M input tokens
β’ $50 / 1M output tokens
[ Read More ] : https://www.anthropic.com/news/claude-fable-5-mythos-5
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#AI #Claude #LLM
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GPT-Live-1 is in the OpenAI API π
OpenAI put GPT-Live-1 in the API. Out of preview. You can call it.
Same full-duplex voice ChatGPT already uses. It listens and talks at the same time, then hands reasoning and tools to a backend model or agent.
What shipped:
β Full-duplex voice sessions you can wire into an app
β Pair it with whatever backend model, tools, or agent you already run
β Built for interruption and overlap, not a turn-detector kludge
You keep your existing model for thinking. GPT-Live-1 is the ears and mouth.
If you ship voice agents, this is the post. Not the GPT-6 Astra work page.
[ Read More ] :
https://openai.com/index/introducing-gpt-live-1-in-the-api
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#AI #API #LLM
@ProgrammingTip
OpenAI put GPT-Live-1 in the API. Out of preview. You can call it.
Same full-duplex voice ChatGPT already uses. It listens and talks at the same time, then hands reasoning and tools to a backend model or agent.
What shipped:
β Full-duplex voice sessions you can wire into an app
β Pair it with whatever backend model, tools, or agent you already run
β Built for interruption and overlap, not a turn-detector kludge
You keep your existing model for thinking. GPT-Live-1 is the ears and mouth.
If you ship voice agents, this is the post. Not the GPT-6 Astra work page.
[ Read More ] :
https://openai.com/index/introducing-gpt-live-1-in-the-api
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#AI #API #LLM
@ProgrammingTip
OpenAI
Build more natural voice experiences with GPTβLiveβ1 in the API
GPTβLiveβ1 brings natural, full-duplex voice conversations to the API, with stronger instruction following, custom voices, and telephony support.
Agents API is in public beta π
OpenAI put the Agents API in public beta. Cloud agents with the Codex harness, managed sessions, tools, and either a hosted sandbox or one you bring yourself.
What shipped:
β Codex harness, not a chat-completions wrapper you have to babysit
β Managed sessions and tools in the API
β Hosted sandboxes, or bring your own
Public beta, so expect sharp edges. If you ship coding agents or anything that needs a real environment, this is the post to read. Not another model card. Call it if you are done stitching sessions, tools, and a VM by hand.
[ Read More ] :
https://openai.com/index/introducing-the-agents-api/
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#AI #LLM #Agents
@ProgrammingTip
OpenAI put the Agents API in public beta. Cloud agents with the Codex harness, managed sessions, tools, and either a hosted sandbox or one you bring yourself.
What shipped:
β Codex harness, not a chat-completions wrapper you have to babysit
β Managed sessions and tools in the API
β Hosted sandboxes, or bring your own
Public beta, so expect sharp edges. If you ship coding agents or anything that needs a real environment, this is the post to read. Not another model card. Call it if you are done stitching sessions, tools, and a VM by hand.
[ Read More ] :
https://openai.com/index/introducing-the-agents-api/
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#AI #LLM #Agents
@ProgrammingTip
OpenAI
Introducing the Agents API
Build and launch cloud agents with the Agents API, a managed service powered by the Codex harness for orchestration, long-running sessions, and tool use.
GPT-6 Astra is OpenAI's new work model π
OpenAI shipped GPT-6 Astra. They are calling it the next generation in intelligence for work.
Not a research teaser. A work model. Expect the name in agent stacks and coding tools.
Why you care:
β New flagship name to put on your eval harness
β Pitched for workplace tasks, not playground chat
β If you pin model ids in config, read this before you swap anything
Start with the official writeup. Then decide if your coding agents still belong on last week's snapshot. I would not change production on a blog title alone. Run your own traces.
[ Read More ] :
https://openai.com/index/gpt-6-astra-next-generation-work
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#AI #LLM #OpenAI
@ProgrammingTip
OpenAI shipped GPT-6 Astra. They are calling it the next generation in intelligence for work.
Not a research teaser. A work model. Expect the name in agent stacks and coding tools.
Why you care:
β New flagship name to put on your eval harness
β Pitched for workplace tasks, not playground chat
β If you pin model ids in config, read this before you swap anything
Start with the official writeup. Then decide if your coding agents still belong on last week's snapshot. I would not change production on a blog title alone. Run your own traces.
[ Read More ] :
https://openai.com/index/gpt-6-astra-next-generation-work
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#AI #LLM #OpenAI
@ProgrammingTip
OpenAI
GPT-6 Astra: The next generation in intelligence for work
Meet GPT-6 Astra, OpenAIβs most capable model for business, with advanced reasoning, computer use, and stronger writing and design judgment.
Make coding agents earn the green check π‘
Do not end an agent task with "implement this." End it with a command that can fail.
For a .NET repo, that might be
A good task contract has three parts:
β’ Change: the files or behavior to update.
β’ Proof: the exact command or test case.
β’ Stop condition: what needs human review instead of another retry.
This also makes PR review faster. You get a diff plus evidence, not a confident paragraph saying the fix should work.
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#AI #dotnet #Testing
@ProgrammingTip
Do not end an agent task with "implement this." End it with a command that can fail.
For a .NET repo, that might be
dotnet test, a focused test project, a formatter check, or a small reproduction script. Tell the agent which command to run, what output to inspect, and what it should do if the command fails.A good task contract has three parts:
β’ Change: the files or behavior to update.
β’ Proof: the exact command or test case.
β’ Stop condition: what needs human review instead of another retry.
This also makes PR review faster. You get a diff plus evidence, not a confident paragraph saying the fix should work.
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#AI #dotnet #Testing
@ProgrammingTip
.NET 11 performance work is landing across the stack π₯
Microsoft published the running list of performance improvements headed for .NET 11. This is not one magic runtime switch. The work spans the runtime, JIT, libraries, and tooling paths that show up in normal application code.
Use the post as a migration checklist:
β’ Find code that is CPU-bound, allocation-heavy, or called per request.
β’ Run your benchmarks on the .NET 11 SDK, not a synthetic microbenchmark only.
β’ Check p50 and p99 latency, allocations, startup, and throughput separately.
The best upgrade wins are boring: existing C# gets faster with fewer code changes. But measure your service. A JIT win can disappear behind JSON, EF queries, network calls, or a container CPU limit.
[ Blog ] :
https://devblogs.microsoft.com/dotnet/performance-improvements-in-net-11/
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#dotnet #csharp #Performance #dotnet11
@ProgrammingTip
Microsoft published the running list of performance improvements headed for .NET 11. This is not one magic runtime switch. The work spans the runtime, JIT, libraries, and tooling paths that show up in normal application code.
Use the post as a migration checklist:
β’ Find code that is CPU-bound, allocation-heavy, or called per request.
β’ Run your benchmarks on the .NET 11 SDK, not a synthetic microbenchmark only.
β’ Check p50 and p99 latency, allocations, startup, and throughput separately.
The best upgrade wins are boring: existing C# gets faster with fewer code changes. But measure your service. A JIT win can disappear behind JSON, EF queries, network calls, or a container CPU limit.
[ Blog ] :
https://devblogs.microsoft.com/dotnet/performance-improvements-in-net-11/
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#dotnet #csharp #Performance #dotnet11
@ProgrammingTip
GitHub moved the Copilot runtime to Rust π
GitHub migrated the runtime behind Copilot to Rust, and used Copilot during the migration itself.
β
Why this is worth reading:
β’ Runtime work is systems work: a language migration means tracing real production behavior, not just translating syntax.
β’ Copilot was part of the workflow: useful for exploring an unfamiliar codebase, drafting changes, and keeping momentum through repetitive conversion work.
β’ The output still needs engineering judgment: boundaries, performance, correctness, and rollout safety are not autocomplete problems.
This is a practical case study for teams asking where coding agents help on a large refactor. Use them to accelerate investigation and implementation. Keep humans on architecture, tests, and production checks.
[ Blog ] :
https://github.blog/ai-and-ml/generative-ai/migrating-the-github-copilot-runtime-to-rust-using-copilot
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#AI #GitHub #Rust #Copilot
@ProgrammingTip
GitHub migrated the runtime behind Copilot to Rust, and used Copilot during the migration itself.
β’ Runtime work is systems work: a language migration means tracing real production behavior, not just translating syntax.
β’ Copilot was part of the workflow: useful for exploring an unfamiliar codebase, drafting changes, and keeping momentum through repetitive conversion work.
β’ The output still needs engineering judgment: boundaries, performance, correctness, and rollout safety are not autocomplete problems.
This is a practical case study for teams asking where coding agents help on a large refactor. Use them to accelerate investigation and implementation. Keep humans on architecture, tests, and production checks.
[ Blog ] :
https://github.blog/ai-and-ml/generative-ai/migrating-the-github-copilot-runtime-to-rust-using-copilot
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#AI #GitHub #Rust #Copilot
@ProgrammingTip
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The GitHub Blog
Migrating the GitHub Copilot runtime to Rust, using Copilot
A rewrite this size wasn't affordable before agents. Here's what porting the Copilot agent runtime to 800,000 lines of production Rust actually took.
Gemini 3.8 Live is generally available π
Google moved Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking to general availability in the Gemini API.
These are the real-time audio models exposed through the
What to check:
β’ Gemini 3.8 Live: the lower-latency voice model.
β’ Extended Thinking: use it when the spoken request needs more reasoning before the reply.
β’ GA status: worth revisiting if you held off on a preview-only voice feature.
For .NET teams, this is a good fit for a streaming WebSocket service, not a request-response controller.
[ Read More ] :
https://ai.google.dev/gemini-api/docs/changelog
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#AI #Gemini #LLM #API
@ProgrammingTip
Google moved Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking to general availability in the Gemini API.
These are the real-time audio models exposed through the
Live API. Live is for the voice path: streaming audio in, streaming audio out, and handling a conversation without bolting together STT, an LLM call, and TTS yourself.What to check:
β’ Gemini 3.8 Live: the lower-latency voice model.
β’ Extended Thinking: use it when the spoken request needs more reasoning before the reply.
β’ GA status: worth revisiting if you held off on a preview-only voice feature.
For .NET teams, this is a good fit for a streaming WebSocket service, not a request-response controller.
[ Read More ] :
https://ai.google.dev/gemini-api/docs/changelog
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#AI #Gemini #LLM #API
@ProgrammingTip
Google AI for Developers
Release notes | Gemini API | Google AI for Developers
Keep track of updates to the Gemini API
GitHub improved the Copilot code review flow π₯
GitHub shipped an improved Copilot code review experience. That matters most on the boring, high-volume PRs where reviewers need help finding a real issue, not another summary of changed files.
For C# teams, use Copilot review as an extra pass on every ASP.NET, EF Core, and library PR. Then keep the human review focused on contracts, failure behavior, and whether the change belongs in the codebase.
Practical rule:
β’ Let Copilot flag suspicious diffs.
β’ Do not merge because it found nothing.
β’ Keep analyzers and tests as the enforcement layer.
AI review is cheap coverage. It is not a replacement for someone who knows why that nullable property or cancellation token exists.
[ Read More ] :
https://github.blog/changelog/2026-09-18-copilot-code-review-an-improved-review-experience
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#dotnet #csharp #GitHub #Copilot
@ProgrammingTip
GitHub shipped an improved Copilot code review experience. That matters most on the boring, high-volume PRs where reviewers need help finding a real issue, not another summary of changed files.
For C# teams, use Copilot review as an extra pass on every ASP.NET, EF Core, and library PR. Then keep the human review focused on contracts, failure behavior, and whether the change belongs in the codebase.
Practical rule:
β’ Let Copilot flag suspicious diffs.
β’ Do not merge because it found nothing.
β’ Keep analyzers and tests as the enforcement layer.
AI review is cheap coverage. It is not a replacement for someone who knows why that nullable property or cancellation token exists.
[ Read More ] :
https://github.blog/changelog/2026-09-18-copilot-code-review-an-improved-review-experience
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#dotnet #csharp #GitHub #Copilot
@ProgrammingTip
The GitHub Blog
Copilot code review: An improved review experience - GitHub Changelog
Copilot code review now gives you a clearer view of how a review changes over time, more intelligently auto-resolves its own suggestions, and generates useful commit messages when you acceptβ¦
Cloudflare saved 100 TB of RAM with math and Rust π₯
Cloudflare reclaimed more than 100 TB of RAM globally in a Pingora-based consistent-hashing service. No new hardware. The win came from changing data representation and the algorithms around it.
β
Lessons:
β’ Measure retained memory, not only allocation rate.
β’ Check collection shape: duplicated keys, oversized buckets, and pointer-heavy graphs add up fast.
β’ Fix the model first: a smaller or more compact structure usually beats micro-optimizing a hot loop.
For high-cardinality caches, routing tables, or tenant maps, take a heap dump before reaching for another cache node. A few bytes per entry becomes expensive at fleet scale.
[ Blog ] :
https://blog.cloudflare.com/saving-100-tb-of-ram-with-math
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#Performance #Memory #Rust
@ProgrammingTip
Cloudflare reclaimed more than 100 TB of RAM globally in a Pingora-based consistent-hashing service. No new hardware. The win came from changing data representation and the algorithms around it.
β’ Measure retained memory, not only allocation rate.
β’ Check collection shape: duplicated keys, oversized buckets, and pointer-heavy graphs add up fast.
β’ Fix the model first: a smaller or more compact structure usually beats micro-optimizing a hot loop.
For high-cardinality caches, routing tables, or tenant maps, take a heap dump before reaching for another cache node. A few bytes per entry becomes expensive at fleet scale.
[ Blog ] :
https://blog.cloudflare.com/saving-100-tb-of-ram-with-math
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#Performance #Memory #Rust
@ProgrammingTip
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Cloudflare Blog
Saving another 100TB of RAM with math (and Rust)
Cloudflare's global network is immense but not limitless. As we look for small ways to trim our resource usage, we sometimes get lucky and we can cut significantly more. Hereβs how we reduced one of our Pingora-based service's RAM usage with statistics.
Hex turns GPT-6 Astra analysis into visual reports π
Hex is using GPT-6 Astra to turn complex analysis into visual reports. The useful bit is not just asking a model to summarize a table. It is moving from an analysis request to a result people can inspect and share.
What this points to:
β’ Analysis as an artifact: teams need charts, assumptions, and outputs, not a chat answer pasted into Slack.
β’ Human review still matters: a clean report can hide bad joins, stale data, or a wrong metric definition.
β’ Tool context is the product: models get more useful when they operate inside the workspace where data and business logic already live.
For AI app builders, this is the bar: produce a result that can survive review, not just a plausible paragraph.
[ Read More ] :
https://openai.com/index/hex-gpt-6-astra
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#AI #LLM #GPT6 #Data
@ProgrammingTip
Hex is using GPT-6 Astra to turn complex analysis into visual reports. The useful bit is not just asking a model to summarize a table. It is moving from an analysis request to a result people can inspect and share.
What this points to:
β’ Analysis as an artifact: teams need charts, assumptions, and outputs, not a chat answer pasted into Slack.
β’ Human review still matters: a clean report can hide bad joins, stale data, or a wrong metric definition.
β’ Tool context is the product: models get more useful when they operate inside the workspace where data and business logic already live.
For AI app builders, this is the bar: produce a result that can survive review, not just a plausible paragraph.
[ Read More ] :
https://openai.com/index/hex-gpt-6-astra
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#AI #LLM #GPT6 #Data
@ProgrammingTip
OpenAI
Hex turns complex analysis into visual reports with GPTβ6 Astra
GPT-6 Astra helps Hexβs data agents turn answers into interactive visualizations that employees are proud to share.
GPT-6 Sol and GPT-6 Luna are in the API π
OpenAI shipped GPT-6 Sol and GPT-6 Luna for API developers, alongside their availability in Codex and ChatGPT.
This is a two-model release, so do not blindly swap your existing production model. Put both behind the same eval set first: tool calls, structured output, long-context retrieval, refusal behavior, and latency under your real prompt size.
What to do this week:
β’ Add Sol and Luna as versioned model options in your config.
β’ Run replay traffic against a fixed golden set.
β’ Log model ID, token use, tool errors, and task success separately.
A model migration is an engineering change, not a dropdown change.
[ Read More ] :
https://openai.com/index/introducing-gpt-6-sol-and-luna/
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#AI #OpenAI #LLM #API
@ProgrammingTip
OpenAI shipped GPT-6 Sol and GPT-6 Luna for API developers, alongside their availability in Codex and ChatGPT.
This is a two-model release, so do not blindly swap your existing production model. Put both behind the same eval set first: tool calls, structured output, long-context retrieval, refusal behavior, and latency under your real prompt size.
What to do this week:
β’ Add Sol and Luna as versioned model options in your config.
β’ Run replay traffic against a fixed golden set.
β’ Log model ID, token use, tool errors, and task success separately.
A model migration is an engineering change, not a dropdown change.
[ Read More ] :
https://openai.com/index/introducing-gpt-6-sol-and-luna/
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#AI #OpenAI #LLM #API
@ProgrammingTip
OpenAI
Introducing GPT-6 Sol and Luna
Meet GPT-6 Sol and Luna, two models that bring frontier intelligence to everyday work with different balances of capability and cost.
Claude Opus 5.5 is now on the Claude Platform π
Anthropic introduced Claude Opus 5.5, a lower-cost frontier model available in Claude Code and through the Claude Platform.
That matters if your agent workload has been split between a high-end model for hard tasks and cheaper models for everything else. A lower-cost Opus tier can change where that handoff happens.
What to check:
β’ Run your existing eval set, not a few cherry-picked prompts.
β’ Measure tool-call accuracy and recovery after a failed call.
β’ Compare total agent cost: tokens, retries, and human review time.
For Claude Code users, model choice is now a practical repo-level config decision, not just a benchmark chart.
[ Blog ] :
https://www.anthropic.com/claude-opus-5-5
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#AI #LLM #Claude #ClaudeCode
@ProgrammingTip
Anthropic introduced Claude Opus 5.5, a lower-cost frontier model available in Claude Code and through the Claude Platform.
That matters if your agent workload has been split between a high-end model for hard tasks and cheaper models for everything else. A lower-cost Opus tier can change where that handoff happens.
What to check:
β’ Run your existing eval set, not a few cherry-picked prompts.
β’ Measure tool-call accuracy and recovery after a failed call.
β’ Compare total agent cost: tokens, retries, and human review time.
For Claude Code users, model choice is now a practical repo-level config decision, not just a benchmark chart.
[ Blog ] :
https://www.anthropic.com/claude-opus-5-5
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#AI #LLM #Claude #ClaudeCode
@ProgrammingTip
Anthropic
Introducing Claude Opus 5.5
Claude Opus 5.5 leads in agentic coding and knowledge work, and costs 40% less to run than Opus 5 on typical workloads.