6imnotracist9

totally not racist

[6imnotracist9 @6imnotracist9]

@nayantaka Saran aj, icons nya jgn pake emoji. Pake lucide icons atau semacam nya🙏

[6imnotracist9 @6imnotracist9]

@mgppap Ai slop gile

[Dark Web Informer @DarkWebInformer]

🚨🇮🇩 A threat actor known as civilian02 is claiming to have leaked project documents tied to Indonesia’s Free Nutritious Food Kitchen MBG program.

The actor claims the leak contains 2.51 GB of data, including 1,384 files and 258 folders. The listing says the exposed material includes architecture, structure, MEP, IPAL, BOQ, RKS, 3D plans, and technical drawings for kitchen project prototypes.

Claim is unverified.

💥 Stop guessing what's redacted. Paid subscribers see everything: https://t.co/281Qjc6p2J

[Modat @modat_magnify]

CVE-2026-44578 
⚠️ Next.js – WebSocket Upgrade SSRF (CVSS 8.6) 

A server-side request forgery vulnerability in Next.js allows unauthenticated attackers to force self-hosted instances to make internal HTTP requests via the WebSocket upgrade handler. 
By sending a crafted absolute-form HTTP request with Upgrade: websocket headers, attackers can access internal services, cloud metadata endpoints, admin panels, and internal APIs reachable from the Next.js server on port 80. Successful exploitation may expose cloud credentials, API keys, secrets, and configuration data. 

Affected: Next.js 13.4.13+, 14.x, 15.x <15.5.16, 16.0.0–16.2.4 

Mitigation: Upgrade immediately to 15.5.16 or 16.2.5.  

Modat Magnify Query: 
technology="Next.js" 

The platform: 
https://t.co/qJfEh7giE9 

#threatintel #vulnerability #CVE202644578 #Nextjs #SSRF #WebSocket #CloudSecurity #infosec #Critical #ModatMagnify

[6imnotracist9 @6imnotracist9]

@mgppap ts sloppy as hell💀💀

- no mobile view support
- emojis as icons
- paywall everywhere
- bad ui/ux visual hierarchy
- sloppy ai patterns everywhere

god damn.

[6imnotracist9 @6imnotracist9]

@mgppap icon msh pake emoji, design ai slop, mobile view support ga ada.

[6imnotracist9 @6imnotracist9]

@F2aldi 2.8?? New model???

[6imnotracist9 @6imnotracist9]

@mgppap Ralph loop.

https://t.co/J8sC60U3r9

[Hugging Models @HuggingModels]

Meet a specialized AI detective for Indonesian text. This BERT model doesn't just understand Indonesian, it identifies and classifies key entities like names, organizations, and locations within sentences. It's a game-changer for processing Indonesian language data.

[Gaffar Al-Ansari🇮🇩🇨🇳🇸🇾 @0xAlansari]

> hindari VPN dibawah ini:

- vpn service yang dibawah payung perusahaan Israel, seperti ExpressVPN, CyberGhost, Private Internet Access, ZenMate, Hotspot Shield, Betternet, Touch VPN.

- Nordvpn, dan Surfshark dari satu payung nord sercurity, juga pernah kena server breach. massive data-marketing.

- Free VPN di app store. alasannya simpel, if the product is free, you are the product.

> Rekomendasi VPN service:

- Proton VPN dan Mullvad VPN

> Kalau kamu sedikit advanced, maka coba:

- Shadowsock atau VMess protocol

> Kalau kamu pengen lebih advanced, maka coba:

- Vless+Reality atau Hysteria2

----

dari pengalaman saya yg sudah lama kesal dengan Great Firewall nya china .

[Qwen @Alibaba_Qwen]

Qwen3-TTS is officially live. We’ve open-sourced the full family—VoiceDesign, CustomVoice, and Base—bringing high quality to the open community.

- 5 models (0.6B & 1.8B)
- Free-form voice design & cloning
- Support for 10 languages
- SOTA 12Hz tokenizer for high compression
- Full fine-tuning support
- SOTA performance

We believe this is arguably the most disruptive release in open-source TTS yet. Go ahead, break it and build something cool. 🚀 Everything is out now—weights, code, and paper. Enjoy. 🧵

Github: https://t.co/X4CNGRpBAG
Hugging Face: https://t.co/QzshIqzYDU
ModelScope: https://t.co/XaWVuDerZ6
Blog: https://t.co/xPER3lyeb5
Paper: https://t.co/9mi5dFyJza
Hugging Face Demo: https://t.co/cL7AyaMDwM
ModelScope Demo: https://t.co/MYpIeYdYN5
API: https://t.co/lIEikdB6uM

[SumitM @SumitM_X]

As a Developer , have you asked yourself how "Typing…" shows up instantly in WhatsApp?

Is the app refreshing every second ?

Or is there something else happening behind the scenes?

[6imnotracist9 @6imnotracist9]

Goated browser fr

[Brave @brave]

If you don't want AI in your search engine, we've got your back.

One simple setting change can hide AI-generated answers and other AI elements from multiple search engines.

Go to Settings &gt; Shields &gt; Content Filtering and enable "Anti-AI Search Filters."

[Tom Dörr @tom_doerr]

Downloads songs and videos from TIDAL via command line or GUI

https://t.co/KITUlTPI41

[6imnotracist9 @6imnotracist9]

@PT_Transjakarta Untuk rute D31 (Cinere - Kuningan), jadwal pemberangkatan pagi sampai jam berapa ya?

[Ahmad @TheAhmadOsman]

> top 8 local LLMs to run at home for December 2025 - updated

> Devstral-2-123B at 1st place
> senior engineer energy
> not flashy, just relentlessly effective
> this is the model i trust when i need results
> just “fix the thing”
> agentic coding is the real superpower
> clean multi-file edits + sane tool use
> massive 256k context actually matters
> big repos, tickets, logs, zero disorientation
> understands the project, not just the file
> dump a repo now, ask follow-ups later
> it remembers why you’re here
> absurd swe-bench for its size (and it’s not 400B+)
> generalizes well, survives messy, undocumented codebases
> closest thing to claude code
> fits on 2x rtx pro 6000
> or 8x 3090s if you’re in your home-lab era
> with the full 256k context
> the exact model i use is cyankiwi/Devstral-2-123B-Instruct-2512-AWQ-4bit

> MiniMax-M2 at 2nd place
> great for agentic workflows thanks to interleaved thinking
> very solid at coding & good at UI/design as well
> fits on 2x RTX PRO 6000
> or 8x RTX 3090s w/ full context
> the exact model i use is cyankiwi/MiniMax-M2-AWQ-4bit

> GLM-4.5-Air at 3rd place
> all-around MVP & daily driver for most tasks that aren’t pure coding
> also, when running multiple agents in parallel, this is my go-to
> uses fewer GPUs & less memory for KVCache
> which allows me to spin up more agents
> fits on a single RTX PRO 6000 or 4x RTX 3090s w/ full context
> nearly as good as big brother, however, not as power hungry
> the exact model i use is cyankiwi/GLM-4.5-Air-Derestricted-AWQ-4bit

> Qwen3-VL-235B-A22B at 4th place
> my main multimodal assistant & visual agent
> very strong with wild dense detection
> massive native context window (256k expandable to 1M)
> we are talking huge documents and long videos with full recall
> sometimes casually out-reasons text-only models
> also great teacher for auto-labeling
> comes in Thinking & Instruct variations
> fits on 2x RTX PRO 6000
> or 8x RTX 3090s w/ full context (256K)
> the exact model i use is cyankiwi/Qwen3-VL-235B-A22B-{Thinking,Instruct}-AWQ-4bit

> GLM-4.6 (REAPed) at 5th place
> the “ok fine, bring out the big brain” model
> very capabable agentic model & coder
> if M2 can’t handle something, i try this one
> fits on 2x RTX PRO 6000
> or 8x RTX 3090s w/ full context
> the exact model i use is 0xSero/GLM-4.6-REAP-218B-A32B-W4A16-AutoRound

> NVIDIA-Nemotron-3-Nano-30B-A3B at 6th place
> this one is sneaky
> looks small on paper, behaves way bigger than it is
> best-in-class SWE / agentic for its size
> consistently punches above its weight on agentic work
> strong, reliable tool use
> clean, predictable structured outputs
> comfortably beats Qwen3-30B-A3B
> absolute trivia sponge
> dumps knowledge on command
> very strong at code + math
> long-context with solid multilingual support
> reasoning-on actually reasons: pause → think → answer
> “spin up five agents without guilt” energy
> low active params = predictable memory
> great throughput
> extremely local-AI friendly

> GPT-OSS-120B at 7th place
> feels like a GPT-5 at home
> big brain, smart and consistent
> agentic, coding, but feels dry in writing

> GPT-OSS-20B at 8th place
> absolute speed monster
> holds up well at tool calling (short context preferred)
> pretty good at instruction following, quick bug fixes, and general use

> Qwen3-Coder-30B as a bonus model
> very capable at coding
> will run on 16GB VRAM quantized
> perfect when you just need something solid for autocomplete

[Xiaomi MiMo @XiaomiMiMo]

⚡ Faster than Fast. Designed for Agentic AI.
Introducing Xiaomi MiMo-V2-Flash — our new open-source MoE model: 309B total params, 15B active.

Blazing speed meets frontier performance.

🔥 Highlights:
🏗️ Hybrid Attention: 5:1 interleaved 128-window SWA + Global | 256K context
📈 Performance:
��️ Matches DeepSeek-V3.2 on general benchmarks — at a fraction of the latency
🏆 SWE-Bench Verified: 73.4% | SWE-Bench Multilingual: 71.7% — new SOTA for open-source models
🚀 Speed: 150 output tokens/s with Day-0 support from @lmsysorg🤝

🤗 Model: https://t.co/4Etm0yZKTL
📝 Blog Post: https://t.co/5zxmcDuB6o
📄 Technical Report: https://t.co/crac1YTLYl
🎨 AI Studio: https://t.co/nSReUs6QgW

[6imnotracist9 @6imnotracist9]

@PT_Transjakarta apa ada grup WA untuk royaltrans rute D31 ya?

[Google Labs @GoogleLabs]

Today, we’re introducing animation in Pomelli! ▶️

Turn content made with Pomelli into on-brand animations with our new ‘Animate’ feature, powered by our Veo 3.1 model.

Now available free of charge in the US, Canada, Australia &amp; New Zealand! Try it now at https://t.co/CIkN8ugZQS

[Avi Chawla @_avichawla]

The real bottleneck isn't compute, it's redundant computation.

Without KV caching, your model recalculates keys and values for each token, repeating work.

- with KV caching → 9 seconds
- without KV caching → 42 seconds (~5x slower)

Let's dive in to understand how it works!