💡 Tip

Before expecting an LLM to handle a complex task, use this litmus test:

Could a human expert — who knows all relevant general knowledge by heart — complete the task in a single go without backtracking, editing, or note-taking?

If not, the prompt may exceed what’s reasonable for an LLM today.


📘 From Prompt Engineering for LLMs: The Art and Science of Building Large Language Model–Based Applications


〰️〰️〰️〰️〰️〰️
#AI #LLM #PromptEngineering
@ProgrammingTip

Anthropic just introduced Claude Fable 5 and Claude Mythos 5 🚀

The interesting part:
Fable 5 and Mythos 5 are basically the same underlying model, but with different access and safety layers.

✅ Claude Fable 5 is the version available for general users. Anthropic says it is their most capable generally available model so far, especially for:

• Software Engineering
• Long-running agentic tasks
• Vision-based work
• Knowledge work
• Scientific reasoning
• 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. 🤯

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. ⚠️

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 💸:
• $10 / 1M input tokens
• $50 / 1M output tokens


[ Read More ] : https://www.anthropic.com/news/claude-fable-5-mythos-5

〰️〰️〰️〰️〰️〰️
#AI #Claude #LLM
@ProgrammingTip

GPT-Live-1 is in the OpenAI API 🚀

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:
✅ Full-duplex voice sessions you can wire into an app
✅ Pair it with whatever backend model, tools, or agent you already run
✅ 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

〰️〰️〰️〰️〰️〰️
#AI #API #LLM
@ProgrammingTip

Agents API is in public beta 🚀

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:

✅ Codex harness, not a chat-completions wrapper you have to babysit
✅ Managed sessions and tools in the API
✅ 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/

〰〰〰〰〰〰
#AI #LLM #Agents
@ProgrammingTip

GPT-6 Astra is OpenAI's new work model 🚀

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:

✅ New flagship name to put on your eval harness
✅ Pitched for workplace tasks, not playground chat
✅ 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

〰〰〰〰〰〰
#AI #LLM #OpenAI
@ProgrammingTip

Gemini 3.8 Live is generally available 🚀

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:

• Gemini 3.8 Live: the lower-latency voice model.
• Extended Thinking: use it when the spoken request needs more reasoning before the reply.
• 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

〰〰〰〰〰〰
#AI #Gemini #LLM #API
@ProgrammingTip

Hex turns GPT-6 Astra analysis into visual reports 🚀

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:

• Analysis as an artifact: teams need charts, assumptions, and outputs, not a chat answer pasted into Slack.
• Human review still matters: a clean report can hide bad joins, stale data, or a wrong metric definition.
• 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

〰〰〰〰〰〰
#AI #LLM #GPT6 #Data
@ProgrammingTip

GPT-6 Sol and GPT-6 Luna are in the API 🚀

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:

• Add Sol and Luna as versioned model options in your config.
• Run replay traffic against a fixed golden set.
• 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/

〰〰〰〰〰〰
#AI #OpenAI #LLM #API
@ProgrammingTip

Claude Opus 5.5 is now on the Claude Platform 🚀

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:

• Run your existing eval set, not a few cherry-picked prompts.
• Measure tool-call accuracy and recovery after a failed call.
• 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

〰〰〰〰〰〰
#AI #LLM #Claude #ClaudeCode
@ProgrammingTip

Claude Opus 5.5 is available in the API 🚀

Anthropic released Claude Opus 5.5, and it is available through the Claude API.

The practical pitch is simple: lower typical token costs than Opus 5, plus a faster mode when response time matters more than squeezing out the last bit of reasoning.

What to check in your evals:

• Run the same tool-use and coding tasks against Opus 5.
• Measure latency separately for normal and faster mode.
• Track input and output tokens, not just the model's listed price.

A cheaper high-end model changes agent architecture decisions. Some workflows that needed routing to a smaller model may now fit under one stronger default.

[ Read More ] :
https://www.anthropic.com/claude/opus

〰〰〰〰〰〰
#AI #LLM #Claude #API
@ProgrammingTip

Claude Sonnet 5.5 is built for coding agents 🚀

Anthropic released Claude Sonnet 5.5 for the Claude Platform. It targets the work developers actually hand to coding agents: navigating a repo, making changes across files, using tools, and checking the result.

What changed:

• Faster agentic work: aimed at shorter tool loops and less idle time while an agent investigates a codebase.
• Lower-cost option: positioned for teams that need to run coding tasks repeatedly, not just ask one-off questions.
• Production focus: Anthropic calls out software engineering and multi-step agent workflows directly.

If your agent spends more time calling tools than writing code, model latency and per-task cost matter as much as benchmark scores. Test it on a real issue queue, with your actual tool permissions.

[ Read More ] :
https://www.anthropic.com/claude-sonnet-5-5

〰〰〰〰〰〰
#AI #LLM #Claude #CodingAgents
@ProgrammingTip

GPT-6 Astra gets an Ultrafast API tier 🚀

OpenAI added an Ultrafast speed tier for GPT-6 Astra in Codex and the API.

The important bit is real-time work. Astra can now run Responses API workflows over WebSockets, which is a much better fit for interactive coding assistants, live agent status, and UI flows where waiting on a full request feels bad.

What to check:

• Responses API: use it for the agent loop and tool calls.
• WebSockets: keep one live connection instead of polling.
• Ultrafast tier: test it where latency matters more than squeezing every last token of quality.

If your app streams agent work to a browser, this is worth benchmarking against your current model setup.

[ Article ] :
https://community.openai.com/t/build-ultrafast-with-astra-in-codex-and-the-api/1402393

〰〰〰〰〰〰
#AI #OpenAI #API #LLM
@ProgrammingTip

OpenAI Decisions API is in public beta 🚀

OpenAI's Decisions API is now in public beta. It uses GPT-6 Luna to turn text or image input into structured decision results instead of another blob of prose.

What it returns:

• Predicates: true or false checks for policy, routing, or eligibility.
• Choices: select one option from a defined set.
• Scores: rank or grade an input on a scale you control.

This is useful when an LLM sits inside a real workflow. Send the result to an approval queue, a feature flag, or a C# switch expression. Keep the model's job narrow, validate the typed output, and do not make your app parse "I think yes".

[ Read More ] :
https://developers.openai.com/api/docs/guides/decisions

〰〰〰〰〰〰
#AI #OpenAI #LLM #API
@ProgrammingTip

Claude Haiku 5.5 is available on the Claude Platform 🚀

Anthropic released Claude Haiku 5.5, its lower-cost small model, on the Claude Platform.

The model ID is claude-haiku-5-5. This is the one to benchmark when your workload has lots of short agent turns: classification, extraction, routing, simple tool calls, and coding tasks where latency matters more than maximum reasoning depth.

What to test first:

• Replace your fast-path model in staging.
• Measure tool-call accuracy, not just chat quality.
• Set a harder-model fallback for failed evaluations.

Small models pay off when they handle the boring 80 percent cleanly. Keep the expensive model for the weird tickets.

[ Read More ] :
https://www.anthropic.com/claude-haiku-5-5

〰〰〰〰〰〰
#AI #LLM #Claude #API
@ProgrammingTip

Give LLM tasks a restart point 💡

Long agent prompts fail badly when the only recovery path is "start over."

Split work into stages that produce an artifact your next call can inspect: a JSON plan, a file list, a patch, test output, or a short decision log. Store that artifact outside the chat context.

Good loop:

• Ask for a plan with explicit acceptance checks.
• Run tools or tests yourself.
• Send back the actual output, not "it failed."
• Ask for the smallest next change.

This also makes retries cheaper. If call three fails, rerun call three with the saved result from call two. Do not pay for the model to rediscover the repository and your constraints every time.

〰〰〰〰〰〰
#AI #LLM #Agents #DevTools
@ProgrammingTip