suren0401

looking for devs(Ai/IoT/web) to do an open-source project

if you guys are intrested then 👉

[suren0401 @surendars0401]

@Balatreado Tbh, these visuals are nowhere near FH 6

[dudu @dudufolio]

Today we're launching  Shotbase

One place for everything Screenshots and Screen Recordings

Capture, trim, annotate, share - no exporting between four tools

Quick ideas, polished demos, team updates, social posts, and everything in between

The capture tool your  Mac should have had

[Sipeed @SipeedLab]

🤖 #AI automated schematic design — what takes a skilled engineer half a day, it finishes in just one hour ⚡.
It’s not just software engineers being replaced, #hardware engineers are next 🔧.
The AI era is hitting hard, and it’s terrifying.
Can we even survive this? 🤔💭

[Daniel Lemire @lemire]

Xiaomi is the Chinese tech giant. Their phones compete with iPhones.

Their new CPU roughly matches Apple cores on single threaded tasks, and is much faster in multithreaded execution. Of course, Apple may soon announce their next processor, so this edge may not last long. And you may find it it difficult to find a phone with the next CPU (Xring O3).

But the new Xiaomi processor is worth discussing further as it reveals an important trend.

The chip has a lot of cache (44 MB in total). It is more than most laptop CPUs. If you have an Intel processor in your laptop, chances are good that you have less cache.

The biggest cores on the the Xring O3 are the C1-Ultra. C1-Ultra really powerful cores. They support SME2 (Scalable Matrix Extension 2) for matrix/AI acceleration, SVE2 for data parallelism (SIMD).

It is astonishingly wide, with 21 execution ports, six of which support SIMD operations (128 bits).

This is more execution ports than you have on your Intel/AMD processor. The AMD Zen 5 has the upper hand because it can do 4x512-bit but 6x128-bit is the best you can do on an ARM chip as far as I know.

So the trend is clear. We are getting cores that are massively parallel in terms of the number of execution units. We get better SIMD (more units) and many more units capable of doing arithmetic.

This means that you can do many, many independent additions or multiplications per cycle. And much more cache.

This is where all the transistors go.

[Dogan Ural @doganuraldesign]

3D LED Icons with Imagine

Here's how ↓

[Sillycorns @Sillycorns]

BREAKING!! 🚨

Snapdragon has just teased “Dual 8 Elites.”

Rumors suggest Qualcomm could launch two flagship processors. Snapdragon 8 Elite Gen 6 and 8 Elite Gen 6 Pro, based on TSMC’s 2nm process with a focus on Agentic AI capabilities

September 22 is going to be interesting. #SnapdragonSummit2026

[Cua @trycua]

1/ Today, we're releasing the first open-source Computer History - now in early preview for Cua Driver on macOS, Windows, and Linux.

It gives agents an encrypted, local record of actions they took through Cua Driver, so new sessions can recover useful context from earlier work.

[Google Cloud Tech @GoogleCloudTech]

https://t.co/ud04fwyFMp

[Prith 🔩🔩 || College Arc @Ronin_VB_King]

Final Boss of all SQL courses 🗿🗿
Planning to finish this along with Hacker Rank course in the next 2-3 weeks

[Zortos @Zortosdev]

LM Studio on GeForce NOW

Gemma 4 31B (41 TPS) on Ultimate Tier

[Tech with Mak @techNmak]

Jay Alammar is the best teacher in AI. Period.

If you have ever seen "The Illustrated Transformer," you know his diagrams are legendary. He also open-sourced the entire codebase for his O'Reilly book: Hands-On Large Language Models.

It’s effectively a visual masterclass in LLMs for free.

Chapter 1: Introduction to Language Models
Chapter 2: Tokens and Embeddings
Chapter 3: Looking Inside Transformer LLMs
Chapter 4: Text Classification
Chapter 5: Text Clustering and Topic Modeling
Chapter 6: Prompt Engineering
Chapter 7: Advanced Text Generation Techniques and Tools
Chapter 8: Semantic Search and Retrieval-Augmented Generation
Chapter 9: Multimodal Large Language Models
Chapter 10: Creating Text Embedding Models
Chapter 11: Fine-tuning Representation Models for Classification
Chapter 12: Fine-tuning Generation Models

I will put the repo link in the comments.

[Vaishnavi @_vmlops]

MACHINE LEARNING VISUALIZED: A FREE JUPYTER BOOK THAT SHOWS YOU HOW MODELS ACTUALLY LEARN

open-source collection of notebooks by Gavin Hung, deriving ML algorithms from first-principles with gifs of them converging during training

→ gradient descent + optimizers
→ PCA + K-Means
→ perceptron + logistic regression
→ neural networks, backprop, loss landscapes
→ autoencoders + latent space

math derivation + working code + visualization in every notebook. MIT licensed, open to PRs

[Chennai Updates @UpdatesChennai]

Tamil Nadu to top-up PM Surya Ghar subsidy; ₹1 Lakh for 3kW rooftop solar

[Sravya Reddy @CodeWithSravya]

50 System Design Topics Every Software Engineer Should Know

1. URL Shortener
2. Rate Limiter
3. Distributed Cache (Redis)
4. Load Balancer
5. API Gateway
6. CDN
7. Message Queue (Kafka/RabbitMQ)
8. Notification System
9. Chat Application
10. News Feed
11. Search Autocomplete
12. Search Engine
13. Distributed Logging
14. Metrics & Monitoring
15. Logging Pipeline
16. File Storage (S3)
17. Object Storage
18. Video Streaming (YouTube)
19. Live Streaming
20. Video Conferencing
21. Ride-Hailing System (Uber)
22. Food Delivery (Swiggy/Zomato)
23. E-commerce Platform
24. Payment Gateway
25. Digital Wallet
26. Stock Trading System
27. Banking System
28. Ticket Booking System
29. Hotel Reservation System
30. Online Auction System
31. Social Media Platform
32. Email Service
33. Push Notification Service
34. Web Crawler
35. Distributed Scheduler
36. Job Queue System
37. Recommendation Engine
38. Analytics Pipeline
39. Event-Driven Architecture
40. Microservices Architecture
41. Service Discovery
42. Database Sharding
43. Replication & Failover
44. Consistent Hashing
45. Distributed Locking
46. Leader Election
47. Circuit Breaker Pattern
48. CQRS & Event Sourcing
49. Real-Time Collaboration (Google Docs)
50. Ad Serving System

[Black Forest Labs @bfl_ai]

FLUX 3 Video is here.

Serious, fun, creative, real, cinematic, whatever you need it to be.

Native audio, Text to Video, Image to Video with multiple frames, video continuation, dialogue in multiple languages.

Comes with Draft mode so you can explore ideas fast at a fraction of the cost.

Up to 20 seconds and 1080p native.

Available in the API or in your favorite tool. 2K, 4K, and Open Weights coming soon.

[Sahil @sahill_og]

When you finally finish that one project

[SamTech @SamTechien]

Daily Routine with claude

[Suraj Sharma @suraj_sharma14]

If I had 6 months to become an Agentic AI Engineer.

I would do this.

Stage 1: Python and Async Foundations
asyncio, FastAPI, event-driven architecture, error handling, API integration patterns.

Stage 2: LLM Fundamentals for Agents
Context management, model routing, token economics, latency tradeoffs, failure modes.

Stage 3: Tool Calling and Structured Outputs
Pydantic validation, function calling schemas, error recovery, dynamic tool discovery.

Stage 4: Memory and State Management
Short-term buffers, long-term vector recall, context compression, cross-session sync.

Stage 5: Single Agent Workflows
ReAct loops, plan-and-execute, self-reflection, iteration limits, graceful degradation.

Stage 6: Multi-Agent Orchestration
LangGraph or CrewAI, supervisor patterns, message passing, conflict resolution, handoffs.

Stage 7: Human-in-the-Loop Systems
Uncertainty detection, approval gates, audit trails, resume logic, intervention points.

Stage 8: Evaluation and Quality Assurance
Automated eval harnesses, LLM-as-a-judge, regression testing, hallucination metrics.

Stage 9: Observability and Tracing
Distributed tracing, cost dashboards, latency monitoring, alerting, debug pipelines.

Stage 10: Security and Guardrails
Prompt injection defense, output filtering, PII redaction, sandboxed execution, compliance.

Stage 11: Production Deployment
vLLM or SGLang, Kubernetes scaling, CI/CD for agents, canary releases, rollback strategies.

Stage 12: Open Source and Portfolio
Ship autonomous agents publicly, write architecture docs, record demos, contribute to libs.

Most people stay stuck watching tutorials.

Builders get hired.

Bookmark it.

[Akshay 🚀 @akshay_pachaar]

As an AI engineer, please learn:

- Learn the roofline model and why decode is memory-bound
- Deploy vLLM and SGLang, then read their schedulers
- Understand paged attention from the code, not the blog post
- Build observability before you optimize anything
- Track TTFT, inter-token latency, throughput, queue depth
- Use Grafana + Prometheus for inference dashboards
- Turn on prefix caching and find which workloads it helps
- Learn continuous batching and chunked prefill
- Run load tests with 1000+ concurrent requests
- Report p50, p95, p99, never just the mean
- Master quantization tradeoffs (FP8, INT4, AWQ, GPTQ)
- Learn speculative decoding and where it stops helping
- Set up KV cache eviction for long contexts
- Try disaggregated prefill and decode serving
- Learn Kubernetes for AI workloads and autoscale on queue depth
- Learn how inference costs break unit economics
- Build your own model router by cost, latency, quality
- Create a token budgeting system per request
- Build one inference service and benchmark it publicly
- Read inference research instead of model release news
- Start sharing your optimization benchmarks

I put together a 10-week plan that covers every one of these at 30 minutes a day. It is 50 sessions, split between reading the theory and building on your own service, and all of them feed one artifact: an inference service you deploy, instrument, load test past 1000 concurrent requests, tune, and publish as a reproducible benchmark.

It is open on GitHub and contributions are welcome, especially newer sources worth adding. I am working through it myself and will share more content on this going forward, so stay tuned.

GitHub repo: https://t.co/UTxKAzhzJQ

(don't forget to star 🌟)

[freeCodeCamp.org @freeCodeCamp]

AI agents can do more than answer questions.

They can plan, make decisions, and automate multi-step workflows.

In this in-depth video tutorial, you'll learn how to build agentic AI apps with LangGraph that can do complex tasks with minimal human intervention.

https://t.co/aS7IwJ5aEt