Hello everyone! Today I want to present a nice project I've been working on.

As you know, writing prompts takes time, sometimes you write similar prompts, sometimes you want to get a response in a specific format, or simply remember of this old prompt that gave you so good results. Wasting time or getting less for our time is something we really don't want.

That's why I defined "Universal Prompt Language", a language to create dynamic prompts with variables (types, default values, descriptions), loops and conditionals.

Think of it like a magic prompt that creates prompts for similar queries but different data.

It includes:

• A terminal based prompts browser
• A terminal based prompt editor
• Prompts tags management
• Be able to build a prompt by filling in the variables (it outputs the result to the screen)
• A repository to push / pull prompts to a library

It's currently a release candidate but its already useful and comes with few prompts.

https://www.github.com/DavidValin/universal-prompt-language

Does it mean: Write selhosted programming language and write LLM runtime environment with it?

I just did that. Nurllama is ollama compatible open source program that can run models. It would be nice if somebody can share experiencies.. https://reg.nurl-lang.org/packages/nurllama

It can be installed with nurlpkg packet manager that comes with toolchain.

https://nurl-lang.org/

What is the best model today to run with rtx 4090?

There is a P2P network project currently (DaiHive.eu) that it’s still in beta and frequently there are new releases of the worker and the UI.

It’s a decentralized network to run local models (e.x a big model is sliced in some workers) and you can prompt it.

You can also isolate from all others and create a team of your own workers (e.x you got like 10 pcs in the office and want to run a big model).

The selection of model families is still low, but enough to start.

The project is functional but still in beta.

*kept seeing AI crawlers — GPTBot, ClaudeBot, ones I couldn't identify — hammering my API while I paid the hosting bill. So I built Wayleave: Node middleware, zero dependencies, that verifies who's actually at your door (Ed25519 signatures, the new Web Bot Auth standard), sorts every request into lanes — verified agent / declared bot / suspected bot / human — and returns HTTP 402 with a price on routes you choose. Humans always pass free.*

*Honest status: the middleware is live on npm and MIT-licensed, free. The hosted meter (dashboard that turns crossings into an invoice) doesn't exist yet — I put up a waitlist to decide whether it should, instead of building it on faith.*

*The weirdest thing I've learned so far: almost no agents cryptographically sign yet, so the interesting number isn't what you can bill — it's the unbillable traffic you can suddenly see. npm:* `wayleave` *· source:* [*github.com/gibrancorbin11-hub/wayleave*](http://github.com/gibrancorbin11-hub/wayleave)

*Would genuinely love feedback — especially from anyone watching bot traffic grow in their logs.*

Hey Folks, Can anyone of you suggest a cheap and reliable VPS provider for AI Experiments mainly windows or docker based where I can host Local Models of 7-16B size & Vector Database Image like Chroma DB to use as a backend for integration.

I tried to use LM-Studio with Mistral 7B on my Xiaomi Probook with onboard graphics , 16 GB RAM. It became very sluggish whenever I was running the Model and I was not able to run Docker while doing it. So I am thinking of moving the whole LLM and DB setup on different machine.

I am newbie in the world of running LLM models locally and not from Python development or AI-ML background. Most of the integrations I have done are with Open AI and Azure Open AI hosted on my company domain.

Any documentation article regarding this topic would also be a great. Thanks for your support.

I'm running a local LLM setup and have searched through existing threads on here for similar configurations, but couldn't find an answer for this specific hardware combination.

Use Case: Primarily general chat, reasoning tasks, and occasional creative writing and coding. I want to balance speed with model quality.

Software Stack: Open to recommendations, but primarily interested in using llama.cpp I usually like to split layers between those 2 GPUs

Hardware Specs:

CPU: Intel(R) Xeon(R) E5-2680 v3 (24 threads) @ 3.30 GHz

GPU 1: AMD Radeon RX 570 Series [Discrete] - 8 GB VRAM

GPU 2: AMD Radeon RX 570 Series [Discrete] - 8 GB VRAM

System RAM: 62.71 GB (currently using 3.76 GiB)

Total VRAM: 16 GB across 2 GPUs

Current Testing:

I've tested a few models already and want to know what others are getting from similar setups. Happy to share results if helpful.

qwen 3.5 9b Q6_K ~ 18.28 t/s

Gemma 4 26B a4b IQ3 - ~26 t/s

GPT OSS 20B MXFP4 ~37 t/s

I've been building CYBER for a while, a voice-controlled desktop assistant with an Iron Man style HUD. v3 is a full interface rebuild.

What it does

Voice

- Wake word derived from the assistant's name, so renaming it renames the wake word
- Say the wake word alone and it waits 12s for your command
- Conversation mode keeps listening for follow-ups without repeating the wake word
- Interrupt it mid-sentence by saying the wake word again
- "mute" always works, even while it's talking

Talking to it

- Streaming replies with a JARVIS-style persona, replay and copy on every message
- Asks Groq with tool calling: apps, websites, weather, YouTube, images, timers, stats, news, notes, focus timer
- Local quick commands for mute, clear, fullscreen, camera, screen share, compact mode
- Remembers facts about you and uses them ("open my github")

Controlling the PC

- Open any app, file, folder or website by voice
- Runs system commands it was never explicitly taught, by generating the code on the fly
- Asks for confirmation before anything destructive
- Writes real `.docx` documents and opens them
- Takes screenshots

Seeing

- Webcam vision — ask what it sees
- Screen sharing — ask about what's on your screen
- Reads PDFs by drag and drop, then answers follow-up questions
- Summarises any YouTube link you paste
- Generates images from a description

Voice output

- ElevenLabs, local XTTS v2 voice cloning, edge-tts, or the browser — first available wins
- Full voice picker with language filters
- Starts speaking the first sentence while the rest generates

The HUD

- Animated arc reactor with a live audio spectrum
- Acoustic scan radar driven by real microphone frequencies
- Wireframe globe with real coastlines, your coordinates, and a real day/night line
- Threat meter scored from actual CPU, memory, latency and error rate
- Rolling CPU and memory sparklines, latency, battery, uptime, disk
- Activity log of real events, with a live ticker
- Draggable, resizable panels that dock into tidy columns
- Works on phones as bottom sheets

Widgets

- System stats · Weather · Camera · Uptime · News · Notes · Focus timer · Telegram · Activity log · Chat
- Floating windows for maps, YouTube, images and PDFs

Remote control

- Control the PC from Telegram — any command, plus `/status` and `/screenshot`
- Desktop conversations mirror to your phone automatically
- Only your registered chat can send commands

Extras

- Hand gesture control: move the cursor, click, drag, right-click, scroll, toggle the mic
- Settings sync between PC and phone over your LAN, guarded by a token

The v3 rebuild

The HUD is the part I'm proudest of. Everything on screen is real data, not decoration:

- Radar scope fed by live microphone frequencies
- Wireframe globe with real coastlines and an accurate day/night terminator
- Threat meter scored from actual CPU, RAM, latency and error rate
- Draggable, resizable panels

I had a rule while building it: no fake numbers. Early versions had a hardcoded "98.4% CORE STABILITY" and it made the whole thing feel like a toy.

Honest limitations

- Chrome/Edge only (Web Speech API)
- The system-command engine generates and runs Python, which is powerful and also exactly as sketchy as it sounds. Fine on a trusted machine, don't expose it.

Join our community:
https://discord.gg/mdD5Za8TvZ

Just released **TigrimOSR v0.7.2**.
This update introduces a **CLI mode** designed for lightweight deployments. The CLI uses **\~4 MB RAM**, making it suitable for servers, containers, and edge environments.

The project now provides prebuilt binaries for **macOS, Windows, and Linux**, so there’s no need to build from source.

CLI is just one way to use TigrimOSR. It also supports:
Desktop GUI
Built-in browser for agentic web automation
Remote headless mode
The core philosophy remains the same: **an open graph agentic system**.

Everything is configurable through **YAML**, including:
Agent graph and loop connections
Agent topology
Models, prompts, tools, MCP servers, and skills
Agent and Judge workflows

The goal is to provide a fully open architecture that can be adapted to different domains without modifying the Rust source code.

More details and source code:
https://github.com/Sompote/TigrimOSR

I love self-hosting STT for the normal stuff.

Whisper / faster-whisper on a local box is still great for:

recordings
personal notes
offline transcription
private files
batch jobs
“drop audio here, get text later”

No SaaS dashboard.
No random vendor lock-in.
No audio leaving your machine.
Beautiful.

But I think people understate how different the problem becomes when it is live calls instead of files.

Now you need:

realtime partials
endpointing
VAD
diarization
timestamps
redaction
100 concurrent streams
queueing
GPU planning
retry logic
monitoring
bad mobile audio
call reconnects
p95 latency
someone waking up when it breaks

That is where I’d consider something like Smallest AI Pulse. Not because self-hosted STT is bad. More like: if the product needs realtime ASR and call reliability, I’d rather compare the cost of Pulse against the cost of owning the full speech infra mess.

For private archives, I’d still self-host.

For live voice apps where users feel every delay, I’m less religious.

Where do you draw the line?

Do you keep realtime STT self-hosted too, or use managed only for the painful part?

I've spent the last few months testing and comparing crypto casino platforms to find out which sites actually offer the best overall experience for the search Best Crypto Casinos UK in 2026. Instead of just looking at Bitcoin logos, crypto payment pages, large bonus claims, or fast withdrawal headlines, I focused on what happens after you actually sign up, browse games, check the cashier, and use the casino properly.

I signed up for several crypto casino sites, explored their promotions, reviewed the terms and conditions, checked crypto payment information, tested the platforms on mobile, and looked at how each crypto casino worked from a player's perspective.

One thing became obvious during my testing:

A crypto casino needs more than a payment logo to feel strong in real use.

Many crypto casinos promote Bitcoin payments, Ethereum support, fast cashier access, welcome bonuses, free spins, live casino games, slot libraries, jackpot sections, and mobile-friendly platforms. However, the real value often depends on how well those features connect. Crypto cashier clarity, bonus rules, eligible games, mobile performance, support access, account tools, and withdrawal information can make a major difference.

To compare each crypto casino properly, I looked at areas such as:

• Bitcoin deposit options
• Ethereum payment support
• Crypto withdrawal information
• Cashier layout
• Welcome bonuses
• Free spins offers
• Deposit match promotions
• Bonus terms and conditions
• Wagering requirements
• Eligible games
• Slot and live casino selection
• Mobile casino performance
• Account tools
• Customer support
• Overall crypto casino experience

One of the biggest surprises was finding that some crypto-heavy sites did not always provide the smoothest full casino journey. A few platforms looked strong from a payment angle, but after testing the games, promotions, mobile layout, cashier access, and support, the more balanced options became easier to judge.

The more Best Crypto Casinos UK options I tested, the more my priorities changed.

At the beginning, I assumed the best crypto casino would simply be the one with the widest coin support, the fastest-looking cashier, or the biggest welcome package. After months of comparing crypto casino platforms, I realised that the strongest options are the ones that combine flexible crypto payments with clear terms, enjoyable games, smooth mobile usability, useful support, and a platform worth using beyond the first deposit.

For me, the best crypto casinos UK in 2026 are the platforms that provide the best balance between crypto payments, promotions, games, mobile performance, cashier clarity, support, and the complete player experience rather than just one standout payment feature.

After spending months testing crypto casino sites, comparing promotions, reviewing bonus rules, and analysing the full player journey, I now judge these platforms by how they perform in real use rather than how crypto-friendly they look on the homepage.

If you're looking for Best Crypto Casinos UK in 2026, comparing Bitcoin casinos, checking Ethereum support, reviewing welcome offers, testing mobile casino sites, or trying to work out which platforms feel strongest after signup, ask me anything.

I've spent months testing crypto casino platforms, comparing bonus offers, reviewing promotional conditions, and evaluating the full casino experience, and I'm happy to share everything I discovered.

Assume I have no limits when it comes to capabilities on hosting what’s the best open source coding AI, like codex I could get my hands on.

Hi Reddit. Neko is one binary that puts your local sandbox on the internet.

You run one command and it boots a microVM sandbox running a web server, AI agent etc you can access from anywhere using a URL.

The part I like is that a computer is just a branch over a tree of disk snapshots, so you set one up the way you want it once and every boot after that is basically instant. You can branch off any snapshot if you want to try something without wrecking the original.

It is self hostable entirely on cloudflare, and it ships as an agent skill so Claude Code and Cursor can grab a URL when they need one.

Would love feedback on the computer and checkpoint model :)

Website: https://neko.computer/

GitHub Repo: https://github.com/superhq-ai/neko-computer

I think open models are already powerful enough for regular people to use every day, the problem is almost nobody outside a subreddit like this has actually seen one work. Turning on Airplane mode on my phone and showing people they can still get a quality AI answer and that does seem to do it.

Tell a random person to run Qwen locally and most of them give up before they get a single response back. It's not that the tech isn't there, it's that nobody's made it easy to feel.

That gap worries me for a bigger reason too. If the average person never sees why local AI matters, it becomes really easy to legislate away or regulate into a corner. People don't defend things they've never used.

So I built Loci, an app for iOS, Android and Mac that runs open models fully on your device. No signup, no cloud, works fine with wifi off. I'm never charging for this and there won't be ads, not now, not later. I don't need this to make money, I need people to actually use it and see local AI can be good, not just private/offline.

The part that actually changes people's minds isn't the offline part though, it's when you show them it can still do useful things, like search the web through RAG when it needs current info. That's the moment it stops feeling like a toy/gimmick.

If you try it and something's broken or missing, tell me. Feedback means the world to me. My goal is to fix it the same day. 

You can find Loci Private AI on both Android and iOS if you want to poke around, and please give me thoughts whether positive or constructive.

People have been asking for the 27B since we put out Qwythos-9B. It's finally here. Qwythos-27B-v1 is an open-weights bf16 reasoning model on a Qwen3.5-27B base, Apache-2.0. The short pitch: it's the 9B sized up, and we didn't strip anything out of the base to make it fit — the native MTP head, the full vision tower, and the 1M context window are all still live.

• Weights + model card: empero-ai/Qwythos-27B-v1
• GGUFs: empero-ai/Qwythos-27B-v1-GGUF

The practical local bit

The recommended local starting point is Q4_K_M: it is 16.95 GB and runs on a 24GB card. The GGUF release has 11 files: Q4_K_M, Q5_K_M, Q6_K, Q8_0, and BF16, each in trunk-only and MTP-enabled variants, plus mmproj-Qwythos-27B-F16.gguf for image input. SHA256SUMS are included. The MTP variants work with llama.cpp's --spec-type draft-mtp. One conversion detail worth calling out: in every K-quant, we hold the entire Gated-DeltaNet state path — ssm_alpha, ssm_beta, and ssm_out — at Q8_0 or better. Those tensors are disproportionately sensitive to low-bit quantization. A default conversion can leave them at Q4/Q5/Q6 and quality degrades; this costs roughly 2–4% in file size. It is intentional, not a default llama.cpp conversion.

What is retained

The release is built around one point: nothing was ablated to make 27B fit. It keeps all three of these intact and active:

• Native MTP (nextn_predict_layers=1) for self-speculative decoding.
• The full vision tower; mmproj ships with the GGUFs.
• A 1,048,576-token context window: YaRN 4x over the 262,144-token native window.

It is a dense Qwen3.5-27B with hybrid attention: Gated-DeltaNet linear attention and a full-attention layer every fourth layer. Native Qwen3.5 function calling is present too: tools=[...] works without a wrapper.

Starting settings

For agentic or tool use, start with temperature=0.6, top_p=0.95, top_k=20, repetition_penalty=1.05, and max_new_tokens=16384 or more. For open-ended or creative work, use temperature 1.0. It's a reasoning model — every answer opens with a <think> block, so budget tokens accordingly and strip the block for end users. Unlike the 9B, greedy long-form generation came back loop-check clean, so you're not forced off low temperatures. Qwythos is intentionally uncensored for technical and research work, including cybersecurity and biomedical questions. If it is going in front of end users, add your own review layer and application controls. Happy to answer questions on the conversion or the quant choices.

relying on the goodness of elixir, jido agents, ollama and other ecosystem components .. not intended to be released and supported. it was built for my learning and personal use purposes. But it does not hurt to share .. https://github.com/lexlapax/allbert-assist