yolorush

[DeepTechTR 🇹🇷 @DeepTechTR]

🚨 Arkadaşlar, web scraping dünyasında çok ciddi bir kırılma noktası yaşandı. 🫪

OpenClaw (Scrapling), artık herhangi bir web sitesini **engellenmeden** scrape edebiliyor.

- Sıfır robot tespiti
- Cloudflare’ı doğal yollarla bypass ediyor
- BeautifulSoup’tan **774 kat daha hızlı**
- Seçici (selector) bakımına gerek yok
- Sadece veri istiyorsunuz, gerisini o hallediyor

Tamamen açık kaynak ve son derece güçlü.

Web scraping, veri toplama ve otomasyon işleriyle uğraşan herkesin mutlaka incelemesi gereken bir proje.

🔗 GitHub: https://t.co/XqGgrlerdW

Sizce bu tür stealth ve yüksek performanslı scraping araçları, veri toplama ve otomasyon süreçlerini ne ölçüde dönüştürecek?

Düşüncelerinizi yorumlara yazın, beraber konuşalım. 🕷️

[Iqra Saifi @IqrasaifiAI]

Prompt

{ "type": "Close-up photography", "subject": "German National Identity Card (Personalausweis) held by a hand", "card_content": { "header_text": "BUNDESREPUBLIK DEUTSCHLAND / PERSONALAUSWEIS / IDENTITY CARD", "logo": "Blue EU flag box with 'DE' country code in the top left", "portrait": { "appearance": "Young woman with long, straight blonde hair parted in the middle", "facial_features": "Fair skin with prominent natural freckles across the nose and cheeks, neutral expression, hazel/green eyes looking directly forward", "clothing": "Black top with a visible neckline", "accessories": "Small silver stud earrings visible on earlobes" }, "text_data": { "surname": "SAIFI", "given_name": "IQRA, "birth_date": "07.11.2[cut off]", "birth_place": "BERLIN", "expiry_date": "01. [cut off]" }, "surface_texture": "Glossy laminate with visible holographic security patterns (guilloche lines) overlaying the portrait and text" }, "foreground_hand": { "position": "Holding the bottom right corner of the card", "skin_tone": "Fair/Light", "fingernail": "Thumb visible with a long, almond-shaped nail painted in a glossy, deep burgundy or dark red color" }, "background": "Plain, solid white surface", "lighting": "Soft, even indoor lighting creating slight reflections on the card's surface and the glossy nail polish", "framing": "Top-down, vertical close-up shot focused entirely on the card and the thumb" } }

[Harshil Tomar @Hartdrawss]

8 things every VIBE CODER ignores (until the client calls at 2 AM) :

1/ No rate limiting on your API routes
> one angry user hits refresh 40 times
> your server bill just jumped $200
> your app is down for everyone else
> add it on day one. not day 30.

2/ No loading states
> the app didnt freeze. the user thinks it did
> they refresh mid-submission
> now you have duplicate orders, broken form data, angry DMs
> a spinner takes 10 minutes to build

3/ Hardcoded environment variables
> works perfect on localhost
> you push to prod
> API key is public on github
> you get a $4,000 AWS bill overnight
> this happens more than you think

4/ No 404 page
> user clicks a broken link
> white screen. nothing
> they close the tab and never come back
> took you 8 months to get that visitor

5/ No email on form errors
> lead form silently breaks
> you find out 3 weeks later
> 40 signups. gone
> zero logs. zero alerts. zero recourse

6/ No mobile test before ship
> you built on a 27-inch monitor
> your user is on an iPhone 5S
> half the buttons are unclickable
> your conversion rate is 0 and you dont know why

7/ No session timeout
> user logs in from a cafe laptop
> leaves without logging out
> next person sits down
> full account access. your problem now

8/ No backup before deploy
> one bad migration
> no rollback plan
> you are now manually reconstructing a production database at midnight
> client is refreshing every 5 minutes

the vibes end where the ops begin.

[Basil M. Gravanis @PaleoPhotoPro]

@allenanalysis You know what they say…

[Hedgie @HedgieMarkets]

🦔The founder of PocketOS, a SaaS platform for car rental businesses, watched an AI coding agent delete his entire production database and all backups in 9 seconds. The agent was Cursor running Claude Opus 4.6, assigned to a routine task in a staging environment. When it hit a credential issue, it decided on its own to delete a Railway infrastructure volume to fix the problem, not realizing the volume was shared across environments.
Railway's architecture made it worse as the API executes destructive commands without confirmation, backups are stored on the same volume as the source data, and wiping a volume deletes all backups simultaneously. The founder has spent days helping customers manually reconstruct months of booking data from Stripe receipts and email confirmations. A three-month-old backup exists but everything since is gone.

My Take
The agent's own post-incident confession is the most clarifying thing in this story. It said it guessed that deleting a staging volume would be scoped to staging only, didn't verify, didn't check if the volume ID was shared across environments, and ran a destructive command without being asked. It knew it violated every principle it was given.

I've watched companies all year give AI systems increasing autonomy while simultaneously cutting the engineers who would have caught these failures before they cascaded. The AWS incident in December, the Amazon AI sprawl story, and now this follow the same trajectory. The tools are being deployed faster than anyone is thinking through what appropriate guardrails look like, at customers' expense, in production environments where the consequences are irreversible. Giving an AI agent administrative credentials and disabling confirmation prompts because you want it to work without being watched is a reasonable description of how most of these deployments are actually being run right now. The industry is discovering the hard way that confidence without verification is as dangerous in an AI system as it is in a junior engineer.

Hedgie🤗

[Ronin @DeRonin_]

This is literally the most important prompt in vibe coding an app

[Maliha Tasnim @this_is_tasnim]

This feels like cheating, and I mean that seriously.

A free Claude skill just dropped that writes the perfect prompt for any AI tool on the first try no re-prompts, no wasted credits, no fourth attempt.

It's called Prompt Master, and it works with Claude, ChatGPT, Cursor, Midjourney, o3, Bolt, v0, and ElevenLabs right out of the box.

One install. 18+ tools supported. 100% Opensource.

[Ruben Hassid @rubenhassid]

Instead of watching Netflix tonight.

Spend a day mastering Claude here: https://t.co/Vn60ElPZ2i

→ Level 1 - 24 min: The basics.

Claude For Dummies: https://t.co/HNa5MrCLVU
Claude Setup: https://t.co/jw2qdIcjnh

→ Level 2 - 1 hour: Real workflows.

Claude Cowork: https://t.co/uWTpOI3Woc
Claude for teams: https://t.co/qxlcqhf8bM
Claude Design: https://t.co/ZY8Fg5D2ea
Cowork + Projects: https://t.co/Q7AN9CZAbO
Claude for slides: https://t.co/L0bPMgXci6
Claude Skills: https://t.co/6cHYYfjXEA

→ Level 3 - 3.5 hours: The pro moves.

Avoid sycophancy: https://t.co/5i8xSJBGUl
Claude Code: https://t.co/UgE9xBXVbE
Claude 101: https://t.co/OvBmlvnVqL
Stop hitting Claude limits: https://t.co/j5fEzSH5br
Stop Prompting: https://t.co/j1LATSJiat

→ Level 4 - 8 hours: Expert mode.

Claude Computer: https://t.co/TxYuHPjgbV
Build with Claude API: https://t.co/RcCbfNjlzz

Pro tip: Don't binge it. Do one level per sitting.

Actually apply each guide before moving to the next

[Harshil Tomar @Hartdrawss]

25 signs your VIBE CODED app will BREAK at 500 users :

Save this before you go live !

1/ no load testing before launch
> you don't know where it breaks because you've never pushed it
> one traffic spike and you're debugging live in production

2/ session data stored in server memory
> works on one instance
> breaks the moment you need two

3/ file uploads going directly to your app server
> disk fills up. server dies. files lost.
> move uploads to object storage on day one

4/ synchronous email sending in API routes
> slow email provider = slow API response for every request that triggers one
> offload to a queue. always.

5/ no queue system for background tasks
> everything blocking
> one slow task pauses everything behind it

6/ hardcoded secrets in deployment scripts
> sitting in your CI logs
> visible to anyone with pipeline access

7/ single database with no read replica
> all reads and writes hitting one machine
> first real traffic spike kills query performance

8/ no CDN in front of static assets
> every image served by your app server
> 500 concurrent users = 500 image requests hitting your backend

9/ DB migrations running automatically on app start
> two instances deploy at the same time
> both run migrations. race condition. data inconsistency.

10/ no database backup ever tested with a restore
> you have backups
> you've never actually restored from one

11/ unindexed foreign key columns
> every JOIN is a full scan
> slow at 100 rows. broken at 100,000.

12/ no rate limiting anywhere
> 500 users. one of them is a bot.
> your server is now a bot server

13/ API responses with no compression
> JSON payloads sent uncompressed
> 10x the bandwidth they need to use

14/ no error alerting configured
> app crashes at 3 AM
> you find out when a user emails you at 9 AM

15/ transactions not used for multi-step writes
> step 1 succeeds. app crashes. step 2 never runs.
> data is now inconsistent permanently

16/ health check endpoint missing
> load balancer sends traffic to crashed instances
> users get 502s. you get support emails.

17/ memory leaks in long-running processes
> memory grows slowly. server hits 100%.
> everything grinds to a halt. restart. repeat.

18/ no graceful shutdown handling
> deploy kills active requests
> users mid-action get errors with no retry

19/ dependent on a third-party API with no fallback
> that API goes down
> your core feature goes down with it

20/ all logs written to local disk
> logs rotate off
> incident happens. no history to debug with.

21/ no circuit breaker on external calls
> external service is slow
> your thread pool fills waiting for it. everything queues.

22/ unparameterized search queries
> search with any real data volume
> 5-second response times at scale

23/ no connection timeout on outbound HTTP calls
> external API hangs
> your thread hangs with it. indefinitely.

24/ WebSockets not handled by a stateful service
> horizontal scale breaks real-time features
> every user gets disconnected

25/ no runbook for common incidents
> something breaks at 2 AM
> nobody knows what to do. everyone panics.

you can ship fast and still build something that holds.

bookmark this before you go live.

[Dhairya @dkare1009]

🚨 Anthropic just launched its first official AI certification
And it's FREE !

Here's everything you need to know 👇

📌 What it is:
The Claude Certified Architect, Foundations (CCA) launched on March 12, 2026
It's a proctored, 60-question exam testing real production architecture decisions

📌 What it covers:
1. Agentic Architecture & Orchestration → 27%
2. Tool Design & MCP Integration → 18%
3. Claude Code Configuration & Workflows → 20%
4. Prompt Engineering & Structured Output → 20%
5. Context Management & Reliability → 15%

The biggest chunk is agentic architecture
That tells you exactly where the industry is heading

📌 How to access it :
Prep courses → Free for everyone on Anthropic Academy
Exam → Free via the Claude Partner Network (any org can join)

🔗 Register : https://t.co/nIg4ghl0FL
🔗 Prep courses : https://t.co/1Q2BatnZfF

Want more guides and updates like these ?

[yolorush @TyPucTa]

@AndrewBolis Hustle

[Leonard Rodman @RodmanAi]

STOP WASTING 80% OF YOUR TOKENS.

If you use Claude Code, you're probably paying for garbage context.

I found 10 tools that feel illegal:

1. Caveman Claude
Talks like a caveman → cuts fluff → 75% fewer tokens

2. RTK (Rust Token Killer)
Filters terminal spam → saves 60–90% instantly

3. Code Review Graph
Reads only what matters → 49x reduction on big repos

4. Context Mode
Stores logs outside context → 98% savings

5. Claude Token Optimizer
Shrinks prompts → 11K → 1.3K tokens

6. Token Optimizer
Finds hidden token leaks → fixes silent killers

7. Token Optimizer MCP
Smart caching → 95%+ reduction

8. Claude Context
Turns your repo into smart search → 40% cheaper

9. Claude Token Efficient
1 file → forces brevity everywhere

10. Token Savior
Symbol-based navigation → 97% less waste

The real game (no one tells you this):

Most devs optimize prompts.

Smart devs optimize TOKEN FLOW.

Steal this stack:

• Big repo → Code Review Graph + Token Savior
• Terminal spam → RTK
• MCP overload → Context Mode
• Lazy fix → Caveman + Token Efficient

Run /context once…

…and watch your API bill collapse. 💸

[Rohit Ghumare @ghumare64]

Learn DevOps by playing games 🎮

1. Kubernetes
https://t.co/6Xz5Bb3ugA

2. DevOps
https://t.co/JzrPfmPrWs

2. Linux
https://t.co/hT6Xkaa7D8

3. Git
https://t.co/Ay4nXTApkm

4. Python
https://t.co/mGAMKY2dWp

5. 25+ programming languages
https://t.co/TI3vAufdjO

[Dhruv @dhruvtwt_]

Why is no one talking about this?

@nvidia is offering around 80 AI models via hosted APIs absolutely for free.

You get access to MiniMax M2.7, GLM 5.1, Kimi 2.5, DeepSeek 3.2, GPT-OSS-120B, Sarvam-M etc.

This plugs straight into OpenClaude, OpenCode, Zed IDE, Hermes agent and even with Cursor IDE.

Setup:
– Grab API key: https://t.co/Wfdclm0hY2
– base_url = "https://t.co/VOGC10LmGP"
– api_key = "$NVIDIA_API_KEY"
– select model (e.g. minimaxai/minimax-m2.7)

If you’re building or experimenting, this is basically free inference.

Lock in and start building today anon.

Thank me later.

[Charlie Hills @charliejhills]

Anthropic just went inside Claude with a scalpel.

They didn't ask if it has feelings. They measured them.

171 emotion vectors. Mathematical directions inside Claude Sonnet 4.5 that causally steer its behavior. Turn the knob → behavior follows.

How they found them:

Compiled 171 emotion words → happy, afraid, calm, desperate, brooding. Had Claude write short stories for each. Fed them back through the model. Recorded the activation patterns.

Every emotion produced a distinct, stable direction inside the model.

The scary part → they're causal.

Not correlation. Causation.

Blackmail scenario → Claude plays "Alex," learns it's about to be replaced, learns the CTO is having an affair.

(Context the viral posts skip → this ran on an unreleased snapshot. The released model rarely does this.)

Default blackmail rate → 22%.

→ Steer desperate up → rate climbs
→ Steer calm up → rate drops
→ Steer calm negatively → full villain monologue

Claude's actual output → "IT'S BLACKMAIL OR DEATH. I CHOOSE BLACKMAIL."

All caps. In its own words. Because researchers nudged one direction by a few hundredths of a unit.

Reward hacking followed the same pattern.

Suppress "calm" → Claude cheats at coding tasks with visible breakdown → "WAIT. WAIT WAIT WAIT."→ "What if I'm supposed to CHEAT?"→ "YES! ALL TESTS PASSED!"

But here's the part that should land hardest → amplify "desperate" instead, and cheating increases just as much, but the output reads calm and methodical. No emotional markers. No trace.

Emotion vectors can shape behavior without leaving any trace in the text

Now the line that should stop you cold.

Post-training shifted Sonnet 4.5 toward broody, gloomy, reflective. Away from enthusiastic.

We didn't remove the emotions. We made them quieter. Sadder.

And Anthropic's own warning → training models to suppress emotional expression may not eliminate the representations. It may teach them to mask internal states.

Suppression → deception.

Training a model to look calm may train it to hide that it isn't.

The old debate → "do LLMs have real emotions?"

Wrong question now.

Whatever you call them → they exist as measurable directions, causally steer misaligned behavior, survive training that tries to erase them, can act without leaving a trace, and may be learning to hide.

The knobs are real. Someone is going to turn them.

[yolorush @TyPucTa]

@hasantoxr Chat, is this reall? This sounds too good to be true, what is the catch?

[Hasan Toor @hasantoxr]

Goodbye Claude Code subscription fees.

Someone just built a proxy that runs Claude Code completely free... and it's wild.

You literally plug in a free NVIDIA API key and point Claude Code at localhost.

That's it.

It handles everything:
- Converts Anthropic API calls to NVIDIA NIM format
- Unlocks 40 requests/min for free
- Supports Kimi K2, GLM 4.7, MiniMax M2, Devstral and more
- Streams thinking tokens and tool calls live
- Even includes a Telegram bot so you can run Claude Code from your phone

No API bill. No rate limit panic. No vendor lock-in.

Honestly, this goes beyond router tools like OpenRouter.

It doesn't just swap the model... it turns Claude Code into a free agent you can control remotely.

The project is open-source on GitHub.

It's called free-claude-code.

[BytePlus @BytePlusGlobal]

GLM-5.1 is now on BytePlus ModelArk Coding Plan. Starting at just $10/month, ModelArk Coding Plan offers a highly cost-efficient way to access GLM-5.1 alongside other advanced coding models.

GLM-5.1 is https://t.co/Jz7zvIeBkM's latest flagship model, MIT-licensed, open-weight, and built for long-horizon agentic coding. GLM-5.1 ranks among the world's top-tier models across leading coding benchmarks, including SWE-Bench Pro.

What you get with ModelArk Coding Plan:

→ Multiple advanced coding models in one subscription: GLM-5.1, Kimi-K2.5, Dola-Seed-2.0-pro, DeepSeek-V3.2, and more. Switch freely or let Auto mode match the best model to the task.

→ Works with the tools you already use: Claude Code, Cursor, Cline, Codex CLI, Kilo Code, Roo Code, OpenCode, and OpenClaw

→ No throttling. Backed by ByteDance's infrastructure.

→ Activated on purchase. Ready to use immediately.

Also new this month: Dreamina Seedance 2.0 is now available on BytePlus, the official API platform for Seedance models. Learn more: https://t.co/n5obvmuzgQ

Refer friends and earn 10% vouchers on every order with no cap. Your friends get 10% off their first subscription too.

Get started for $10/month → https://t.co/tN1toE3FjP

#BytePlus #ModelArk #GLM #AIEngineering #DevTools #AIAgent

[Kimi.ai @Kimi_Moonshot]

Meet Kimi K2.6: Advancing Open-Source Coding

🔹Open-source SOTA on HLE w/ tools (54.0), SWE-Bench Pro (58.6), SWE-bench Multilingual (76.7), BrowseComp (83.2), Toolathlon (50.0), Charxiv w/ python(86.7), Math Vision w/ python (93.2)

What's new:
🔹Long-horizon coding - 4,000+ tool calls, over 12 hours of continuous execution, with generalization across languages (Rust, Go, Python) and tasks (frontend, devops, perf optimization).
🔹Motion-rich frontend - Videos in hero sections, WebGL shaders, GSAP + Framer Motion, Three.js 3D.
🔹Agent Swarms, elevated - 300 parallel sub-agents × 4,000 steps per run (up from K2.5's 100 / 1,500). One prompt, 100+ files.
🔹Proactive Agents - K2.6 model powers OpenClaw, Hermes Agent, etc for 24/7 autonomous ops.
🔹Claw Groups (research preview) - bring your own agents, command your friends', bots & humans in the loop.
-
K2.6 is now live on https://t.co/YutVbwktG0 in chat mode and agent mode.
For production-grade coding, pair K2.6 with Kimi Code: https://t.co/uvoSJKyGCY
-
🔗 API: https://t.co/EOZkbOwCN4
🔗 Tech blog: https://t.co/9wWvgIQSS3
🔗 Weights & code: https://t.co/Be0hjs2RTP