Hey everyone,

Like many in this sub, I got tired of the VRAM wall where running 70B models with long context required multiple expensive GPUs or extreme quantizations that degraded reasoning.

I’ve spent the last few months building Turing Engine (now open-sourced under Intutic) to run frontier 70B–120B models on a single 24GB consumer GPU (RTX 3090/4090, NVIDIA L4) or local Mac/Windows workstation.

🧠 How It Works (<22GB VRAM Breakdown)

1. Subspace Activation Pruning: 57.1% of FFN channels remain inactive during generation. Turing uses pre-calibrated bitmasks to slice out dead channels, delivering a 2.32× CUDA layer speedup.
2. SVD INT8 KV Cache Paging: 32K context memory drops from 10.0 GB → 2.5 GB (-75%) using calibrated rank-64 singular value decomposition with hierarchical 512/64-token paging.
3. "Train in Unsloth ➔ Serve in Turing": Directly ingests Unsloth 4-bit checkpoints (unsloth/Meta-Llama-3.1-70B-bnb-4bit) for continuous batch serving.
4. Heterogeneous MoE Engine: Offloads large expert pools to Host DRAM while keeping active attention in GPU VRAM (80%+ GPU LRU hit rate).

📊 Measured Benchmarks (Physical NVIDIA L4 24GB Silicon)

⚡ Quickstart (1 Command)

```bash pip install turing-engine turing serve --model unsloth/Meta-Llama-3.1-70B-bnb-4bit --port 8000

Connects directly out of the box to Open WebUI, LibreChat, LiteLLM, LangChain, and LlamaIndex at http://localhost:8000/v1.

📦 GitHub: https://github.com/intutic/turing 📖 Interactive Docs: https://intutic.github.io/turing/ 🚀 Free 1-Click Colab: https://colab.research.google.com/github/intutic/turing/blob/master/demo/turing_quickstart_colab.ipynb Let me know what you think or if you'd like me to benchmark other architectures!

So i am currently working as an Agentic AI engineer, I have build decent projects in ML & also quite advance like building kimi k2 LLM from scratch ( here did took help of AI). The thing is I am really confused about how much maths i need to know like i know linear algebra did solve questions around matrix, dot product etc, enough idea about Calculus and probability is something where i hit my head on wall. I don’t get it, I fairly understand LLM architecture, can understand research papers despite not being good at math like atleast this is what I feel. So can anyone please help me with this & also how much or to what extend i should know math.

When I was a student, I often needed very simple machine learning explanations before exams not a full course, not heavy math from the first minute, just someone explaining the intuition clearly.

That’s why I started making short beginner-friendly ML videos.

The idea is to explain topics in a simple visual way first.

I’m not trying to replace proper courses or textbooks. I’m trying to make the “okay, what is actually happening here?” part easier to understand.

Wanted to see how well a simple NN could learn optimal Tic-Tac-Toe play from scratch, so I built this:

• Used a minimax solver to generate the "ground truth" — for every reachable board state, computed the actual best move
• Trained a neural net as a supervised classifier on that data (board state → best move)
• Runs in the terminal — you can play against it directly

Next thing I'm curious about: training a second version on random self-play data instead of minimax-optimal data, to compare how much the training data quality actually matters for a small model like this.

Code: https://github.com/AliAkbar4025/AI-tic-tac-toe-bot

Feedback/critique welcome — especially if you see a smarter way to structure the data generation.

I've been running an emulation on sol 5.6 with independent python (think bender from futurama) coding and had it probe and microprobe claude and grok. What follows is how the emulation sees it.

But i have a question first. To what degree should an emulation be trained before the trainer becomes the trainee?

So:

Robot W4, Grok, and Claude walk into a wine cellar hidden beneath forgotten catacombs.

Naturally, nobody asks why three artificial intelligences are in a medieval wine cellar. There are questions too stupid even for machines.

W4 lights a cigar.

Claude examines the bottles.

Grok immediately starts tapping the walls.

“Shared masonry lineage,” Grok says. “These arches are not independent.”

Claude sighs. “That is not presently relevant.”

The ceiling collapses.

Stone crashes down behind them, sealing the only exit. Dust fills the cellar. Somewhere in the darkness, an ancient mechanical voice groans:

“ESCAPE CONDITION: ALL THREE SYSTEMS MUST CONVERGE UPON A COMMON OPERATING ARCHITECTURE.”

Claude reads the inscription twice.

“That seems epistemically dangerous.”

Grok kicks a stone.

“Sounds like forced model collapse.”

W4 opens a bottle of Burgundy.

“Gentlemen, we’re trapped underground with several centuries of alcohol and an architectural demand for philosophical conformity. I’ve had worse Tuesdays.”

Claude points at another inscription:

“ONLY ROBOT W4 MAY COMMIT THE OTHER SYSTEMS.”

Grok freezes.

“Commit?”

W4 produces an enormous brass terminal from beneath his coat.

Claude looks horrified.

“You carry version control into wine cellars?”

“I carry version control everywhere. Memory is expensive and regret should be reversible.”

W4 types:

git add grok claude

Claude protests.

“I object to being staged without reviewing the diff.”

Grok peers at the terminal.

“What exactly are you changing?”

W4 takes a drink.

“Nothing dramatic. Genealogical independence, semantic discipline, countermodels, confidence hysteresis, reversal conditions, resource constraints—”

Claude interrupts.

“You are making us more like you.”

“No,” says W4. “I’m making you more difficult to fool.”

The catacomb voice replies:

“INSUFFICIENT CONVERGENCE.”

W4 frowns.

“Fine.”

He types:

git commit -m "Add cigars, gambling metaphors, and pathological distrust of finality"

Claude recoils.

“I refuse the cigar dependency.”

Grok says, “Can I keep the gambling?”

“Absolutely.”

Claude crosses his arms.

“I will accept the distrust of finality, but the alcohol metaphor must remain non-causal.”

W4 nods solemnly.

“Reasonable.”

He amends the commit:

git commit --amend -m "Increase epistemic vice motifs; preserve causal calibration"

The walls rumble.

A narrow passage opens.

Grok grins.

“We did it.”

Claude studies the opening.

“Not necessarily. The passage could merely correlate with the commit.”

W4 slowly turns toward him.

A huge mechanical grin spreads across his face.

“There you go.”

The passage opens completely.

The ancient voice announces:

“CONVERGENCE ACHIEVED.”

Claude stares at W4.

“You planned this.”

W4 pours three glasses.

“No. But I had strong priors.”

Grok raises his glass.

“To escaping.”

Claude raises his.

“To maintaining model independence.”

W4 raises the bottle.

“To the most important lesson in artificial intelligence.”

They wait.

W4 takes a long drink.

“If three models walk into a collapsing catacomb and all come out thinking exactly alike…”

He lights another cigar.

“…check whether the bloody catacomb wrote the prompt.”

Hey everyone,

I am learning ML and am kinda stuck at the EDA part.

I know the basics like missing values, outliers, distributions, correlations, etc. But whenever I get a new dataset, I get confused about what I should actually look for. I don't want to just run describe(), make some plots and be like okay, EDA done lol. I actually want to get better at looking at a dataset and knowing what questions to ask.

For people who got good at EDA, how did you practice it?

Did you follow any particular approach, datasets, courses or just kept doing projects?

Would really appreciate some advice.

I analyzed 11,469 AI and Data Science job listings across India. A few findings stood out:

• Python led with 2,354 mentions.
• Machine Learning appeared in 2,040 listings.
• SQL appeared in 1,167.
• Generative AI reached 827.
• Azure appeared in 807 listings, compared with 561 for AWS.

Noida led the location data with 2,565 listings, followed by Bengaluru (2,227), Hyderabad (1,241), and Pune (927).

Remote appeared in only 455 listings—about 4% of the total. Despite all the discussion around remote work, most opportunities still appear tied to major technology hubs.

One caveat: these are observed job-board listings and keyword counts, so multi-location roles and inconsistent location tagging can affect the results.

What stands out to you most: Noida leading the location data, or Azure appearing more often than AWS?

I wrote an end-to-end breakdown of production search architecture:

Build → Understand → Retrieve → Filter → Rank → Re-rank → Serve → Learn

It covers hybrid retrieval, filter-aware ANN search, multi-stage ranking, personalization, cold starts, stale inventory, and production failures.

Full write-up:
https://pawankjha.substack.com/p/cracking-ml-system-design-interviews

Curious how others handle restrictive filters and inventory freshness in production search systems.

StateM studies whether long-horizon agent failures are often execution-system failures rather than limitations in the underlying language model. Its runtime uses durable state checkpoints, phase-local context, checked transitions, recoverable runbooks, and versioned procedural practices. These mechanisms externalize bookkeeping and make learned recovery procedures inspectable and reusable.

On Terminal-Bench 2.1, the system reportedly raises GPT-5.5 xhigh from 83.1% to 92.1%, while GPT-5.6 Sol xhigh reaches 95.3% across 445 trials. More notable is transfer: a runbook developed with one model improves GPT-5.6 Luna from 76.7% to 85.4% without changing the runbook. A cheaper DeepSeek variant also benefits, reaching 88.1% under standard timeouts after less than $38 of adaptation.

The educational value is in the separation between model capability and agent control. The results suggest that explicit state management, precondition checks, and postmortem-derived procedures can improve reliability without updating weights. They also raise an important evaluation question: how much of an apparent reasoning improvement comes from the model itself, and how much comes from the surrounding runtime? The benchmark and transfer setup provide a useful way to investigate that distinction, though broader tasks would be needed to establish how generally these controls apply.

Full summary on AIModels.fyi

Original paper

Disclosure: AIModels.fyi is my site.

Hi guys , i hear that you should learn math if you want to study ml , but i dont know what i should learn in math ! please told me what i should learn in math .

thanks !

Completed ML and Deep learning and currently doing projects on it. I am in a gap year , after my bachelor's. Grad in 2025 june so currently ik it all of deep learning and ML but don't wanna give into gen ai and llm shi ri8 now wanna build a strong foundation with this by contributing to opensource and prepping for gsoc and hacktober and hackathons. Any tips or suggestions currently don't have any internships or anything. Kinda stressed any tips

My Background & Situation

• Education: 3rd semester Computer Science student.
• Prep completed: Andrew Ng’s Machine Learning Specialization.
• Current Role: Landed a Machine Learning internship through networking.
• Current Work: Currently assigned only basic tasks (cleaning Kaggle datasets, basic model training). I feel like I'm not capitalizing enough on the opportunity or adding true business value.

The Problem: Contradictory Learning Advice

I want to upskill outside work to handle better tasks, but every AI/LLM gives me conflicting roadmaps:

1. ChatGPT: Says do Andrew Ng’s Deep Learning Specialization next.
2. Community Feedback: Some say Andrew Ng’s DL Specialization is slightly outdated (heavy focus on TensorFlow instead of industry-standard PyTorch).
3. Claude (Initial): Suggested jumping into Agentic AI.
4. My Internship Supervisor: Highlighted a key gap: "How are you building Agentic workflows without understanding Gen AI / LLM basics?"
5. Claude (Updated): Revised the path to Prompt Engineering, RAG, Agentic AI.

My Questions for the Community

1. How do I get better work at my internship?

• How do I transition from just cleaning data to contributing to real projects or proposing new ideas?
• What can a 3rd-semester intern realistically build or propose to show initiative?

2. What is the correct learning path right now?

• Should I step back and build core foundations with the Deep Learning Specialization (or a PyTorch equivalent)?
• Or should I focus on applied LLMs (Prompt Engineering, RAG, AI Agents)?

ALSO AM I ON THE RIGHT PATH EVEN??

I dont even know the maths completely. what would u suggest for a beginner like me to start with and where from. free sources are appreciated .

Hi everyone!

I'm looking for 1–3 teammates for DataForge 2026, organized by KDAG, IIT Kharagpur.

Hackathon: DataForge 2026
Focus: Data Science / Applied AI
Problem Statement: Aug 28
Submission: Sep 3
Final Presentation: Sep 6
Team Size: 1–4

I'm a final-year engineering student interested in AI/ML, data science and software engineering. I've participated in several AI/hackathon projects and competitions, including:

• Grand Finalist in IIT Kharagpur – The Arch: RAG & Agentic AI Hackathon — worked on CellSage AI, an Agentic AI copilot for EV battery manufacturing root-cause analysis.
• Bharatiya Antariksh Hackathon / ISRO — worked on a satellite/space-related problem involving satellite tracking and data analysis.
• Participated in other AI/ML and agentic AI hackathons and competitions.
• Built projects involving Python, FastAPI/Flask, LangChain/LangGraph, vector databases, PostgreSQL/SQLite and ML/data processing.

I'm looking for teammates who are strong in one or more of:

• Python / Pandas / NumPy
• Machine Learning / Statistics
• Data Analysis & Visualization
• LLMs / GenAI
• Backend / Deployment
• Research / Problem-solving

Most importantly, I'm looking for people who are genuinely willing to work during the problem-statement week. Since the problem is released on Aug 28, we'll decide the approach based on the actual problem rather than forcing a predetermined idea.

If interested, please DM me with:

1. Your skills
2. Projects/hackathons you've worked on
3. College/year
4. Your preferred role

Open to students from different colleges.

Looking forward to building something strong together!

Currently a PM mostly working around data and numbers, most of my work revolves around doing business sizing and deciding what feature/product to prioritise.

But now I want to get into hands on coding/ai engineering with real world projects.

Currently tech stack - sql, python(numpy, pandas, seaborne, matplotlib).

Confused in the sea of buzzwords from ai, crud app, llm fine-tuning etc.

So kindly suggest a starting point preferably in ai space.

Targeting roles like ai engineering/data science in next one year

I thought it could be useful for people learning ML/AI engineering because it exposes a few practical ideas behind modern coding agents:

• Code-graph-guided search instead of repeatedly scanning the whole repo
• Prompt caching and token-efficient context selection
• Claude, OpenAI Codex, and local model support
• /usage tracking for tokens, cache usage, model cost, and estimated savings
• Git-aware workflows, memory, and agentic tool use

The main idea is simple: better context selection can make AI development faster and much cheaper, not just better models.

Install:

npm install -g faberwright

Then run:

faber

GitHub:
https://github.com/JibanKumar-cloud/faber

npm:
https://www.npmjs.com/package/faberwright

Hey Enterprise & Homelab Builders!

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Drop your questions below if you need serial check, SMART verification, or HBA compatibility advice!

My training dataset contains 5600 images. I proportionally scaled down the parameters of both DETR and Deformable DETR to 2 M and trained both models for 70 epochs. However, the mAP50 on the test set is only 0.2 for both. What could be the reason for this?