Sarmad Ahsan

DevOps | DevSecOps | Kubernetes | Docker | Linux | Jenkins CI/CD | Ethical Hacker | AWS | Python | RHEL | Bash | Git | Ansible | Terraform

[Lisan al Gaib @scaling01]

I know a thing that I'm actually number 1 in the world

I'm 2/2 in reporting first (minutes before the official launch) about 10T+ model system cards

I was first for GPT-4.5
and
for Claude Fable 5

[Micky @Rasmic]

I spent $1,000 on Claude Fable 5 (Mythos with a seat-belt) and here are my thoughts...

[CJ Zafir @cjzafir]

Claude Fable 5 took my 4 months of fine-tuning work and made an end-to-end 7-stage pipeline that i can sell.

It took 3 hours of /goal.

> Built TUI,
> html dashboard,
> html dataset viewer,
> crafted 39 special skills,
> wrote 8700 lines of code,
> ran 235 tests.

And the pipeline in 98% ready in one-shot. Shipping this soon.

[Google DeepMind @GoogleDeepMind]

DiffusionGemma is our new experimental open model with up to 4x faster output on dedicated GPUs.

Instead of predicting word-by-word, it generates entire blocks of text simultaneously. This lets the model self-correct and format complex markdown in real time.

[Fuli Luo @_LuoFuli]

A strong model evolution needs a solid harness system, and vice versa. 14 days, 5 people, one vibe-coding journey — and MiMo Code was born. It's open source: https://t.co/Yb0aPX5IOH

[Xiaomi MiMo @XiaomiMiMo]

🚀 MiMo Code V0.1 is now live and open-source！

More than an AI coding assistant in your terminal — it's the smartest coding partner you'll ever work with.

Comes with MiMo V2.5, a multimodal model available free for a limited time, featuring a million-token context window—ready to use out of the box.

♾️ Infinite Context: Knowledge accumulates automatically, and with lossless compression, even million-line projects keep every critical detail intact—quality never drops.
🧠 Agent-Model Synergy: An Agent framework deeply optimized for MiMo, with a full closed loop of testing, review, and validation—so complex tasks get done in one pass.
📝 Compose Mode: Specs → Plans → Build → Report. Design first, code second—clear thinking, no rework.
🔄 Self-Evolving System: Every session is automatically reviewed, distilling experience and best practices—the more you use it, the smarter it gets.
🎙️ Voice Input: Powered by MiMo-V2.5-ASR — just speak instead of type, and your voice becomes the prompt for truly hands-free coding.
🔌 Claude Code Compatible: Automatically loads your existing skills, MCP servers and commands, and reuses your API configuration—zero-cost migration, no setup required.
🌐 Open & Flexible: MIT licensed, with support for leading model providers including Anthropic, OpenAI, DeepSeek, Kimi, GLM and more.

Install in one line:
Mac & Linux
curl -fsSL https://t.co/ViHsb4eGns | bash
(For the best experience，we recommand Mac user use it on iTerm or vscode terminal)
Windows
npm install -g @mimo-ai/cli

🔗 Learn more
Website ↓
https://t.co/Aq9BVuyA9a
Blog ↓
https://t.co/f40wLgQicK
GitHub ↓
https://t.co/O5Mj3rzl9g

[Saiyam Pathak @SaiyamPathak]

Bookmark this: Local LLM resources for 2026

- llama.cpp: inference + GGUF
- Hugging Face Hub: model cards + weights
- Ollama: easiest local start
- r/LocalLLaMA: hardware + benchmarks
- Ahead of AI: deep explainers
- KubeSimplify: DGX Spark series

Pick one. Build this week.

[freeCodeCamp.org @freeCodeCamp]

If you want to show that you have a deep understanding of Kubernetes admin, this course is for you.

It'll help you study for (and hopefully pass!) the Kubernetes Administrator Certification exam.

You'll learn about cluster architecture, role-based access control, workloads and scheduling, networking, and lots more.

https://t.co/bABLEwDr4b

[Artificial Analysis @ArtificialAnlys]

Claude Fable 5 launched today at #1 on the Artificial Analysis Intelligence Index, putting Anthropic nearly 5 points ahead of any other lab’s best model

We supported @AnthropicAI with pre-release evaluation of Claude Fable 5. Claude Fable 5 scores 64.9 on the Artificial Analysis Intelligence Index, claiming the #1 rank overall. It is ~5 points ahead of the closest non-Anthropic model (GPT-5.5), and Anthropic models now occupy both of the top 2 places.

Key takeaways for Claude Fable 5 (adaptive reasoning with max effort and Opus 4.8 as fallback model):

➤ New safety guardrails for Mythos-class models: Claude Fable 5 uses the same underlying model as Claude Mythos 5 for public usage, with additional guardrails for potentially-harmful cybersecurity, biology, chemistry, and distillation-related queries. We tested Fable 5 using Anthropic’s new ‘fallback’ mechanism, which can route safety-flagged messages to Claude Opus 4.8. Anthropic states that fallback occurs in fewer than 5% of sessions on average, and we recorded fallback routing in ~8% of tasks across the Intelligence Index (mostly in scientific questions from evaluations like GPQA, AA-Omniscience and Humanity’s Last Exam)

➤ State-of-the-art Intelligence: Claude Fable 5 takes the #1 position on the Artificial Analysis Intelligence Index, scoring 64.9 and setting the highest score on 5 of the 10 underlying benchmarks. On AA-Omniscience, our knowledge and hallucination benchmark, Fable 5 scores 40, +7 points over the previous leader, Gemini 3.1 Pro Preview, driven primarily by higher accuracy. We generally observe a strong relationship between AA-Omniscience accuracy and model size in open weights models, which suggests Fable 5 could be larger than previous public Anthropic models

➤ Frontier agentic capability: Claude Fable 5 is at the frontier across all three agentic evaluations in the Index: GDPval-AA (real-world work tasks), Terminal-Bench Hard (agentic coding), and Tau2-bench Telecom (tool use for customer service). Its GDPval-AA Elo of 1932 is a significant jump from the previous leader, Claude Opus 4.8, further extending Anthropic’s lead in agentic capabilities

➤ Leading HLE score, but refusal and fallback in 9% of tasks: Claude Fable 5 scores 53% on Humanity’s Last Exam, more than 7 points ahead of the next-best model, Claude Opus 4.8 (max). Fable 5 triggers safety guardrails on 9% of HLE tasks, falling back to Claude Opus 4.8. Including this fallback usage, running HLE with Fable 5 costs ~$2.2k, the highest of any model we have evaluated

Key model details:

➤ Context window: Claude Fable 5 retains the same 1M token context window as Claude Opus 4.8

➤ Price: Claude Fable 5 is priced at $10/$50 per 1M input/output tokens, 2x the token price of Claude Opus 4.8. The cache write/read price is $12.50/$1 per million tokens

➤ Availability: Claude Fable 5 is included in Pro, Max, Team, and seat-based Enterprise plans through June 22, consuming 2x Opus usage. From June 23, usage will require credits, with Anthropic saying it plans to restore subscription access once capacity allows

[ZenMux @ZenMuxAI]

Claude Fable 5 is now live on ZenMux 🚀

Positioned as a Mythos-class Claude model, Fable 5 is built for heavier workflows where follow-through matters:

💻 autonomous coding
📚 document-heavy analysis
🔁 complex multi-step workflows
🤖 long-running agent tasks

For teams using Claude Code, it is a strong fit for handing off longer tasks and reducing manual check-ins.

Free to try for all new ZenMux users:
https://t.co/vNWpvtxbfL

[Rohan Paul @rohanpaul_ai]

Interesting claim from SemiAnalysis.

AI subscriptions are dramatically underpriced versus API usage:

- For heavy coding/chat users, the subscription can be 40–70× cheaper than paying API rates; the API is mainly better when you need automation or product integration.

- a $200/month ChatGPT Pro plan can provide about $14,000/month of API-equivalent usage, while a $200/month Claude Max 20x plan can provide about $8,000/month.

[Isra @israfill]

you can use unlimited FREE deepseek v4 pro & flash, minimax m3, gemini 3.5 flash with zero credit card 😳

b. ai giving 500k free credits on signup, no verification needed

what you get:
- deepseek v4 pro & flash
- gemini 3.5 flash
- minimax m3
- more frontier models

it's openai compatible so you can plug it into any agent or bot. you just swap the base url and your existing tools work

full setup guide (bai 500k free credits):

step 1: go to https://t.co/pK3GidYD3R
> click "try b. ai" in the top right

step 2: sign up with google
> any gmail works, burner is fine.

step 3: credits appear instantly
> 500,000 in your account. no card, no verification need

step 4: swap your base url
> replace your openai endpoint with https://t.co/pK3GidYD3R's api endpoint
> all your existing tools and agents just work

step 5: repeat with a fresh email when you run out
> use [email protected], [email protected] etc
> each account gets 500k fresh credits
> incognito browser for each one

there's also a 1:1 top-up bonus up to $100 if you run through it all

meaning you deposit $10, they give $10 free extends your credits without creating new accounts

3 things to test with 500k credits:
1. stress test your agent pipeline across 4 models at once
2. compare model outputs side by side to find your cheapest option
3. run batch inference on a dataset without worrying about cost

this is the most generous free credit offer live right now

bookmark this and get this before the subsidy pool runs out

[██████RD @stanxq]

@cursor_ai The most impressive model on this list:

[Egor K. @egorkabantsov]

Claude Fable 5 benchmarks are kind of wild.

- SWE-Bench Pro: 80.3%
- Terminal-Bench 2.1: 88.0%
- OSWorld: 85.0%
- ExploitBench: 78.0%
- HLE: 64.5% with tools
- FrontierCode Diamond: 29.3%

That last one is CRAZY.
- Opus 4.8: 13.4%
- GPT-5.5: 5.7%
- Fable 5: 29.3%

[Vaibhav Sisinty @VaibhavSisinty]

Anthropic’s new Mythos-class model is 3x cheaper than OpenAI’s best model.

And it beats it on nearly every benchmark. 🤯

GPT-5.5 Pro: $30 input / $180 output
Claude Fable 5: $10 input / $50 output

The benchmarks:

→ Agentic coding: 80.3% vs 58.6%

→ FrontierCode (code quality): 29.3% vs 5.7%

→ Cybersecurity: 78.0% vs 34.0%

→ Legal: 13.3% vs 2.1%

→ Reasoning: 59.0% vs 41.4%

→ Health: 66.0% vs 51.8%

→ Computer use: 85.0% vs 78.7%

Cheaper. Better. On almost everything.

Free on Pro and Max plans through June 22. It’s called Claude Fable 5.

[XDA @xdadevelopers]

I finally ditched Ubuntu Server after five years, and realized why Fedora's release cycle actually wins for production
https://t.co/v8Rhw3M9si

[Ahmad Awais @MrAhmadAwais]

Command Code effect on Vercel AI Gateway as we launched in public beta 1st May and grew to trillions of tokens repairing over 40% of DeepSeek’s tool calls and became one of the most used coding agent harness for open models.

[Shraddha Bharuka @BharukaShraddha]

Most developers think Claude Code is just an AI coding assistant.

They're wrong.

Claude Code is secretly a 5-layer operating system for AI agents.

And 90% of people never go beyond Layer 1.

Here's the architecture 👇

━━━━━━━━━━━━━━━

🧠 Layer 1 — Memory (CLAUDE.md)

Your project's brain.

Stores:
→ Coding standards
→ Architecture decisions
→ Team workflows
→ Repo conventions
→ Engineering rules

Persistent across sessions.

This is what transforms Claude from:

"Generic AI"

into:

"Your engineering team's AI."

━━━━━━━━━━━━━━━

📚 Layer 2 — Skills

Reusable expertise modules.

Need:
→ A React expert?
→ A security auditor?
→ A database architect?

Claude dynamically loads the right knowledge only when needed.

Benefits:
→ Cleaner context
→ Lower token usage
→ Specialized execution
→ Fewer hallucinations

This is where AI starts feeling agentic instead of conversational.

━━━━━━━━━━━━━━━

🔒 Layer 3 — Hooks

The layer most teams completely ignore.

Hooks are programmable infrastructure triggers.

Examples:
→ Auto-run tests
→ Block risky commands
→ Enforce code quality
→ Inject runtime context
→ Send Slack alerts
→ Auto-format outputs

This is NOT AI reasoning.

It's deterministic reliability.

Production-grade AI systems are built here.

━━━━━━━━━━━━━━━

🤖 Layer 4 — Subagents

This is where Claude Code becomes a true multi-agent system.

Delegate tasks like a real engineering organization:

→ One agent writes code
→ One reviews PRs
→ One writes tests
→ One investigates bugs

Parallel execution.

Isolated context.

Separate tools.

No context pollution.

No recursive chaos.

You stop thinking:

"One assistant"

And start thinking:

"Distributed cognitive workers."

━━━━━━━━━━━━━━━

📦 Layer 5 — Plugins

The distribution layer.

Package:
→ Skills
→ Hooks
→ Commands
→ Agents
→ Workflows

...into one reusable install.

Install once.

Share across teams.

Reuse everywhere.

This is how organizations operationalize AI engineering at scale.

━━━━━━━━━━━━━━━

The biggest misconception in AI right now:

People think the magic is prompting.

It's not.

The real leverage comes from:

→ Architecture
→ Orchestration
→ Memory systems
→ Deterministic workflows
→ Agent coordination

Most people are chatting with Claude.

A few are building autonomous software teams inside it.

That's the real shift happening right now.

🔖 Bookmark this if you're serious about AI engineering.

#ClaudeCode #AIAgents #AgenticAI #AIEngineering #SoftwareDevelopment #LLM

[Kimi.ai @Kimi_Moonshot]

Meet Kimi Work - a local AI agent on your desktop that does the work for you.

🔹Native agent swarm: Up to 300 AI agents running in parallel on your local machine.
🔹Browser use: Paired with WebBridge extension, your agent will navigate websites in your browser: search, scroll, click, type and complete tasks.
🔹Built for Finance: Native global market data tool call from Yahoo Finance and World Bank - no complex API setup required.
🔹Memory system: Kimi Desktop keeps a running diary of your preferences, past decisions, and context to know you better.

Available for macOS (Apple Silicon) and Windows.

🔗Try it now: https://t.co/yhiai2VWIy

[Crystal @crystalsssup]

Kimi desktop version is here!

Available for macOS (Apple Silicon) and Windows.
Try it 👉 https://t.co/DLSRDYG8vD