Armas

Shine different from the crowd !

[Cloudflare @Cloudflare]

We are expanding the Clef decision model family with Clef-omni, natively processing audio, video, images, and text in a single pipeline. We’ve also lowered Clef-flash pricing and boosted Clef inference speeds by up to 2.0x. https://t.co/UKDp0OKHHA

[Armas @armas_shaikh_08]

@OfficialLoganK @GoogleAIStudio @grok compare with @liquidai omni decision model

[Armas @armas_shaikh_08]

Keep Android Open (Stop Google from limiting APK file usage) - Sign the Petition! https://t.co/c6yNJRDQs6 via @ChangeOrg_India

[Armas @armas_shaikh_08]

@maritime_sh @grok compare pricing with agent37

[Armas @armas_shaikh_08]

@msbr_dev @Vultr CODE

[Armas @armas_shaikh_08]

Hey @NextDNS ,I am having issues with cloudflare verification and google auth on sites . Work normally on other dns. Please look into this.

[RT @RT_com]

'436 CHILDREN... 436 LIVES'

Iranian media RELEASES VIDEO showing PHOTOS of 436 CHILDREN ‘MARTYRED’ in 'US & ZIONIST REGIME-IMPOSED WARS’

[BlockRunAI @BlockRunAI]

OpenJev is now FREE on @BlockRunAI .

A 4B cross-encoder that answers instead of writing:
1. yes/no, a labelled choice, or a score over rungs you name.
2. Up to 64 questions in one call. Deterministic — same text, same number.

https://t.co/uzQ6UY7fwu

[solidSF @solidSF]

nobody is asking us to do these, we just love to compete

[Armas @armas_shaikh_08]

@_shiblee Sab bhosdike bhaiya

[freeCodeCamp.org @freeCodeCamp]

Most agent tutorials are organized by domain, like healthcare and finance, as if the agents are built differently underneath.

But in this full book, Vahe argues that's misleading: if you strip away the prompts, every agent runs the same handful of architectural patterns.

He catalogs 60 of them across eight capabilities like memory, planning, and tool use, with code and failure modes for each.

https://t.co/Im6oXuQTBc

[freeCodeCamp.org @freeCodeCamp]

Knowledge graphs are useful when the questions you care about are really about relationships and multi-hop connections.

In this handbook, Roni shows you how to build one with Python and Neo4j.

You'll learn all about graph modeling, data loading, indexing, query tuning, and how knowledge graphs can support AI systems.

https://t.co/mQMrM5Ers6

[Meng To @MengTo]

I open-sourced ThreeUI, my library of three.js components and landing pages.

160+ are free, and the tool is free too. It includes procedural 3D hero sections, icons, and motion designs. Copy the prompt or source, give it to your agent, then change the theme, lighting, motion or layout.

Live site: https://t.co/rtCfqeZnOP

Repo: https://t.co/B66ahgxHVl

I'm adding a lot more soon. Pro comes with 50+ extra components, MCP, and skills. Early adopters get 50% off right now.

[freeCodeCamp.org @freeCodeCamp]

Data structures and algorithms can feel abstract until you can actually picture how they work.

In this course, Sumit teaches DSA using visual explanations and real-world analogies.

You'll learn about arrays, linked lists, stacks, queues, hash tables, trees, graphs, searching, sorting, recursion, dynamic programming, and more.

https://t.co/mf6pKMIdU7

[Armas @armas_shaikh_08]

@Badtheorylabs Can't log in to your website ?

[V E C T O R @VectorPrivacy]

Privacy Messengers 2026 Infographic by @FormlessLabs

- Updated as of 8/15/2026
- Added Open-Source Category

Available on:
https://t.co/uLgMNzLEY2

[Armas @armas_shaikh_08]

@Mutchtaba2 Agentic rag but I don't use a single model for visual understanding I use minimax me ,architecture planning goes to glm for research and deature engineering kimi does the best for me and the implementation goes to DeepSeek flash or pro depending upon the complexity.

[Armas @armas_shaikh_08]

@CommandCodeAI 🙋🏻‍♂️

[freeCodeCamp.org @freeCodeCamp]

Data analysis is a popular and growing field in the tech world.

And this 19-hour course takes you on an in-depth journey, whether you're a beginner or more advanced in your skills.

You'll learn about Python, Excel, SQL, Tableau and Power BI & much more.

https://t.co/v6skwMV7lc

[Akshay 🚀 @akshay_pachaar]

LLM engineer's handbook

(30 minutes a day, 10 weeks, 50 lessons)

a roadmap for llm inference serving where everything points at one service instead of scattering across demos. you get the mental model first, then serve a model, instrument it, load test it past 1000 concurrent requests, and tune it. you finish with a stack you configured yourself and a benchmark worth publishing.

here is what it covers:

→ the roofline model, and why decode waits on memory while prefill waits on compute

→ vLLM internals, PagedAttention and the scheduler, read from the code

→ Prometheus and Grafana for TTFT, inter-token latency, and queue depth

→ SGLang and RadixAttention prefix reuse, benchmarked against vLLM

→ load testing past 1000 concurrent requests

→ quantization across FP16, FP8, and INT4, on quality as well as speed

→ speculative decoding and KV eviction, including where the gains disappear

→ disaggregated prefill and decode, deployed on Kubernetes

→ a cost, latency, and quality router with per-request token budgeting

→ publishing a reproducible benchmark

the roadmap on GitHub: https://t.co/pOqkWvDJ2d

(don't forget to star 🌟)

i am also writing an article for each major topic. the first one is out, on how a GPU actually works.

the article is quoted below.