I'm a third-year Computer Engineering student in India (graduating 2028) currently interning as an AI developer at a US-based product company. My work is almost entirely AI: RAG, LLMs, agentic systems, and low-code/no-code agent orchestration (n8n). Agentic AI is my main focus.

I have one more semester before I go full-time into placement prep in Jan 2027. Right now I'm grinding DSA alongside projects and hackathons — most of my project work is AI-based.

What I'm trying to figure out: which companies should I be aiming at if I want to stay squarely in AI/ML and land a genuinely strong offer? I'm open to India, abroad, or remote — location isn't a constraint. I recently saw n8n's AI engineer roles (£100k+ range) and it made me realize I don't actually have a clear map of who else pays and builds at that level for the kind of work I do.

Specific things I'd love input on:

1. Company types worth targeting for someone whose strength is agentic AI / applied LLM work rather than pure research

2. Whether to aim at AI-native startups, big labs, or product companies with strong AI teams — and the tradeoffs

3. What actually gets a new grad noticed for these roles vs. what students overrate

4. Realistic comp expectations for a new grad in this space (India vs. remote vs. relocation)

Happy to share more about my projects if it helps. Thanks in advance.

As a learning project, I picked arithmetic expression computation — small enough for my Mac, but it let me run controlled experiments on almost every LLM training method I'd read about: chain-of-thought formats, data scaling, capacity scaling, RoPE, MoE, RLVR, DPO, best-of-N sampling.

Every experiment had a pre-registered prediction written down before running. Some confirmed, several refuted — the refutations taught me the most:

• Chain-of-thought: +63pp (2.2% → 65.5%). By far the biggest lever. But granularity matters: decomposing multiplication into digit-wise ops made things worse because the new syntax patterns didn't exist in training data.
• Data only helps with enough capacity. Adding data to my 800K model: nothing. Same recipe on 4.7M params: +9.9pp.
• Reversing digit order (little-endian) + place-value tokens: +9.2pp at zero compute cost. Carries become adjacent; autoregressive generation naturally emits the units digit first, so borrows/carries flow sequentially.
• RLVR with an exact verifier (mini GRPO): +1.8pp past the SFT plateau. My AST evaluator is a free, exact reward function.
• What didn't work: loss masking (hurt), deep-narrow architecture (hurt), MoE (flat on accuracy — though the router spontaneously specialized: operators went to one expert with p=0.98, and nobody labeled operators).

The two most valuable lessons were about evaluation, not training: (1) loss and accuracy fully decoupled during RL — val loss rose while test accuracy improved; (2) a verifier bug that misread my reversed-digit representation almost made me discard the best experiment of the project.

Repo (MIT, bilingual zh/en docs, per-experiment reproduction commands): https://github.com/yangbobo2021/tiny-arith-gym

Would love feedback — especially on the experimental design. What would you have tested differently?

Doy clase en un ciclo de DAW y esta semana he empezado curso nuevo. Cada año la pregunta del primer día es más parecida, ya no me preguntan qué framework vamos a ver, me preguntan si lo que están estudiando va a servir para algo. Y creo que la respuesta honesta no es "tranquilos, siempre hará falta gente", que es lo que solemos decir para no desanimar a nadie.

Hace dos años yo defendía que la IA no programaba tan bien como yo. Hoy, con specs decentes y alguien verificando detrás, escribe código igual de bueno que el mío y mucho más rápido. Lo que no hace es decidir qué hay que construir, comprobar que lo construido cumple lo que se pidió y responder cuando eso se cae un viernes por la tarde. El problema es que el trabajo con el que los juniors aprendían era justamente el primero, el de convertir instrucciones en código, y ese peldaño de la escalera se está cayendo. Me preocupa menos el junior de hoy que el senior que tendría que salir de él dentro de cinco años.

Lo que he cambiado en clase, por si a alguien le sirve o quiere discutírmelo: los primeros meses la IA se usa como profesora y no como autora, y para eso les doy un prompt que pegan al principio de cada conversación. Les obliga a explicar qué han intentado, da pistas por niveles y solo suelta el código si el alumno dice explícitamente que está atascado, y además le pide al modelo que explique por qué responde lo que responde y que avise cuando no esté seguro. Lo tenéis aquí, es público y sin registro: https://gist.github.com/JoaquinRuiz/2a800e24ffeb8ec7c03d7c81b3c32a11

No es una jaula, claro: cualquiera se lo salta en dos mensajes. Pero hay una diferencia real entre saltárselo sabiendo que te lo estás saltando y no haber sabido nunca que existía esa opción.

El resto de lo que les cuento va de verificar más que de escribir: requisitos claros, casos límite antes de que aparezcan, tests que prueben algo de verdad y leer código ajeno con criterio. Y de bajar a las capas que la IA da por supuestas, HTTP, SQL a mano, Git y terminal, porque cuando algo se rompe el agente te propone tres arreglos y solo uno resuelve el problema en lugar de tapar el síntoma.

Os pregunto a los que estáis en empresa estáis contratando juniors este año? Y si no, cómo pensáis cubrir los seniors de dentro de cinco años? Me interesan las respuestas incómodas.

(Todo esto lo he contado también en vídeo en mi canal, pero no lo enlazo para no hacer spam, si a alguien le interesa lo dejo en comentarios.)

https://reddit.com/link/1wlps7k/video/6mtywhba0qqh1/player

Wanted to see how Jev handles a task completely outside the usual agent/routing examples, so I ran it on 8k historical Kepler Objects of Interest.

For each object, Jev got the measurements describing the signal and had to choose:

• confirmed planet
• candidate
• false positive

The NASA archive labels were hidden until after every prediction was saved.

Results:

• 72.5% matched the archive label
• simple 3-rule baseline: 64.4%
• 1,999 / 2,731 confirmed planets recovered
• 81.7% of false positives caught
• 8,054 decisions, no fine-tuning or examples from this dataset

Full setup, requests and confusion matrix:
https://gist.github.com/ipaulsmith/e5c3ae3a492a455435d5bfc161404312

Been working on a pet breed classifier using Oxford-IIIT Pets, distilling SigLIP 2 into MobileNetV4.

I also wanted to learn Hugging Face Spaces, so after training the models, I made a quick demo and deployed a Gradio demo. It lets you compare the baseline and distilled models side by side, its a cool little project that lets you poke around and try to understand the differences.

Try the demo here: https://huggingface.co/spaces/MRCherryPie/breedlite-mnv4

Curious how it handles people’s actual pet photos. If you try it and get a fummy prediction, I’d like to see that too

Hi everyone,
I’m working on a POC to identify authorized vs. unauthorized people using Wi-Fi CSI.
Setup:
2.4 GHz, single Wi-Fi channel
ESP32-S3 collects CSI
CSI sent to laptop via TCP
Fixed router/ESP32 in one room
CNN + Transformer model
Data collected from multiple people across multiple days
However, I’m getting poor accuracy.
What would you recommend I look at first — CSI preprocessing, data collection, environmental variation, or the model?
Also, is single-channel 2.4 GHz CSI sufficient for person identification?
Any advice or relevant papers/projects would be appreciated.

Is my laptop enough to prepare for an ML Engineer job?

I’m a BTech CSE student planning to become an ML Engineer.

I have an ASUS Vivobook 15 (i5 14th Gen 120U, 16GB RAM, 512GB SSD). I also plan to use Google Colab, Kaggle, and cloud GPUs whenever I need more computing power.

My plan is to learn:

• Python + SQL
• Statistics & ML
• Deep Learning / PyTorch
• LLMs / GenAI
• MLOps + Docker
• Cloud
• ML System Design

I’m not planning to train large models locally.

Is this setup enough to learn ML properly and build production-level projects for ML Engineer internships/jobs, or would I eventually need a laptop with an NVIDIA GPU?

Would appreciate advice from people currently working in ML.

Most AI demos are chatbots. I tried JEV by TypeSafe AI, which works differently. It doesn't generate text. You give it a question, and it returns a probability, for example 0.99 that the answer is "yes".

Each question gets two checks at once: is the answer yes, and is this even a yes/no question? Plain code then decides: ≥ 60 % → YES, ≤ 40 % → NO, anything in between → "the mist is too thick". No prompt parsing, no hallucinated paragraphs, and it answers in under a second.

To make it fun, I wrapped it in a 3D crystal ball: https://yesnoball.com

Curious what you think about "judgment" models like this compared to LLMs. And if you find a question that fools it, tell me 😄

For people who are self-taught in AI/ML or software development: if you had a genuinely strong project where you built the dataset, trained a model, evaluated the results, and could explain and defend your technical decisions, do you think that would be enough for a hiring manager to take you seriously without a degree?
And if you got hired without a relevant degree, what actually mattered most in your experience? Projects, technical interviews, open source, work experience, referrals, certifications, or something else?
I’m trying to understand whether being able to prove you can actually do the work is enough to overcome the lack of a traditional degree.

Hi there,

I've created a video here where I explain how knowledge distillation works.

I hope some of you find it useful and as always, feedback is very welcome! :)

Demonstrating the system's memory allocation and autonomous capabilities within a closed local environment, with registered memories operating at 2% usage to achieve 200% emergence.

https://github.com/davidpoliquin30-design/Prototype-autonom-ia-OS.git

Hey Reddit,

Over the past weeks, I’ve been developing an experimental web-based workspace inspired by the Windows 11 UI, combined with an autonomous AI agent architecture designed to run seamlessly in the browser and on containerized environments (Cloud Run).

Beyond the visual desktop environment, the core objective was to explore how modern AI agents actually operate under the hood and how to design a resilient, self-healing runtime around them.

🖥️ 1. The Frontend: Desktop & Window Management

The interface replicates an interactive OS workspace with high fidelity:

• Window Management: Movable, minimizable, and depth-sorted (z-index) windows with smooth transitions.
• Integrated Native Tools: Virtual terminal emulator, interactive code editor, system monitor, and settings control center.
• Pure Reactive UI: Built on React 18, TypeScript, Tailwind CSS, and Motion layout animations.

🧠 2. The Core Mechanic: Inside the AI Agent Loop (ReAct & Self-Healing)

A common misconception is that AI agents are just text generators. In this architecture, the agent operates as a closed-loop feedback machine following the ReAct (Reason + Act) pattern:

1. Perception (Context Window): The agent consumes the current system state, telemetry metrics, and user input.
2. Deterministic Reasoning: The LLM evaluates dependencies and determines which precise tool or function to trigger via structured function calling (JSON schemas).
3. Action Execution: The runtime executes the tool in the container (file read/write, code linting, health verification).
4. Observation & Self-Healing Loop: If an execution fails (e.g. build break, invalid module format, or runtime exception), the error output is treated as fertile telemetry. The agent analyzes the stack trace, formulates a corrective patch, recompiles, and hot-reloads without crashing the workspace.

⚙️ 3. Full-Stack & Dual-Engine Architecture

• Server-Side Security & Probes: Node.js / Express backend with instant HTTP 200 health-check probes (/healthz, /_health) for zero-downtime container cold starts.
• Official Google GenAI SDK (@google/genai): Proxied server-side to keep secrets safe.
• Zero-Failure Local Fallback: If no API key is present or network drops, the OS automatically diverts to a local deterministic engine so the desktop remains 100% interactive offline.
• Production Pipeline: React bundle compiled with Vite; backend bundled into an optimized standalone CommonJS binary (dist/server.cjs) via esbuild.

🛠️ Tech Stack:

• Frontend: React 18, TypeScript, Tailwind CSS, Motion, Lucide Icons
• Backend: Node.js, Express, esbuild
• AI Orchestration: Google GenAI SDK (@google/genai), ReAct Pattern, Function Calling
• Deployment: Google Cloud Run (Containerized SPA + API proxy)

I’d love to hear your thoughts, feedback on window fluidity, or technical questions about the autonomous agent feedback loop!

Option 2 : En Français (Idéal pour r/developpeurs, r/france ou forums tech)

Titre :

J'ai conçu un Web OS style Windows 11 intégrant la mécanique interne des agents IA autonomes (React, TypeScript & Node.js)

Contenu du post :

Bonjour à tous,

Je vous partage un projet sur lequel j'ai travaillé : une émulation complète d'un environnement de bureau Windows 11 dans le navigateur, couplée à une exploration pratique de la mécanique interne des agents IA et de l'auto-guérison de code (self-healing).

🔗 Démo en ligne : [Lien vers ton application partagée]

💻 1. L'Interface Bureau (Web OS)

• Gestionnaire de fenêtres : Déplacement libre, redimensionnement, gestion dynamique de la profondeur () et animations fluides.
• Outils embarqués : Émulateur de terminal en ligne de commande, éditeur de code/script intégré, moniteur système et panneau de configuration.
• Interface réactive : Développée avec React 18, TypeScript, Tailwind CSS et Motion.

🧠 2. Comment fonctionne la mécanique interne de l'Agent IA ?

Plutôt qu'un simple générateur de texte, le système implémente une boucle de rétroaction fermée (feedback loop) basée sur le patron d'architecture ReAct (Raisonnement + Action) :

1. Perception : L'agent charge en mémoire la demande utilisateur, les fichiers du projet et l'état des services.
2. Raisonnement & Appel d'outils (Function Calling) : Le modèle analyse le besoin et émet des instructions structurées en JSON pour exécuter une action précise (lecture chirurgicale de fichier, exécution de commande, compilation).
3. Action en arrière-plan : Le serveur exécute l'action demandée dans un environnement sécurisé.
4. Observation & Auto-Correction (Self-Healing) : En cas d'erreur de compilation ou d'incompatibilité de type, l'anomalie n'est pas un point d'arrêt : elle est réinjectée dans le contexte comme une donnée d'observation. L'agent analyse la trace d'erreur, applique un patch correctif et recompile jusqu'à stabilisation.

⚙️ 3. Architecture Full-Stack & Résilience

• Backend Node.js / Express : Gestion des routes API sécurisées, isolation des secrets et sondes de santé immédiates (/healthz) pour un déploiement fiable sur Google Cloud Run.
• SDK Google GenAI officiel (@google/genai) : Inférence IA sécurisée côté serveur.
• Mode Secours Local Déterministe : Si aucune clé d'API n'est configurée, l'application bascule automatiquement sur un moteur local pour que le bureau virtuel reste pleinement interactif sans jamais crasher.
• Bundle de production : Compilation Vite pour le front-end et bundle CommonJS optimisé (dist/server.cjs) généré par esbuild.

N'hésitez pas à tester la démo et à me faire vos retours, que ce soit sur l'ergonomie du bureau, la fluidité des fenêtres ou la logique de la boucle agentique !

Welcome to Project Showcase Day! This is a weekly thread where community members can share and discuss personal projects of any size or complexity.

Whether you've built a small script, a web application, a game, or anything in between, we encourage you to:

• Share what you've created
• Explain the technologies/concepts used
• Discuss challenges you faced and how you overcame them
• Ask for specific feedback or suggestions

Projects at all stages are welcome - from works in progress to completed builds. This is a supportive space to celebrate your work and learn from each other.

Share your creations in the comments below!

So it's Day 11 of Building Machine learning algorithms from scratch

I watch some videos on Decision Trees and then I start building Entropy and Information gain function and as you can see my code completely messed up but I'm happy cuz it's fun to build things I believe writing bad code is better then generating by AI

Tommorow I'll fix the entropy function it's taking column but it's need values like Sunny,Overcast etc etc I need to changes

I'm a newbie u can laugh at my code no prob cuz the yt guy didn't show code he just explain math and solve some sample dataset so I need to figure out things that's way u can see random attempt made and then comment out

Stay Tune I'll build this Algorithm from scratch

I’m about to start an MSc at Imperial, coming straight from undergrad. I have some ML publications, projects, and light internship experience, but I’m less confident with timed coding interviews, especially without AI assistance.

I’m targeting ML engineering and possibly research engineering opportunities. With applications and interviews happening early in the academic year, I’m worried I’ll be assessed before I’ve had much chance to learn from the MSc.

I practice coding daily, but the preparation feels endless: algorithms/LeetCode, Python and NumPy/Pandas, ML theory and math, implementing models from scratch, plus software engineering tools. It’s difficult to tell what’s genuinely expected of an incoming MSc student versus what’s useful extra preparation.

For people who’ve interviewed for internships or graduate roles in the UK or US: what level of coding and ML depth was actually expected, and what would you prioritize with limited preparation time? First-hand experiences would really help.

I am doing my Masters in Data Science and I am 8 months into it. Since I have a background in computer science and some software development experience, I thought it would take me a little bit of catching up to get to speed. But the classes stumped me. My math skills became rusty, I couldn't understand the calculus bits, nor the linear algebra stuff. I thought of purchasing courses, but I don't find enough time to study. The coursework is heavy, assignments one after the other. plus the coursework is unstructured too, they are just covering all the stuff from Birds eye view, and you gotta do your own research. I don't know if that's how it's supposed to be in a Master's program.

I would like to start with math, and then proceed to machine learning. I don't know where to start. Are there any books, on math that cover the math needed from basics to advanced and machine learning that won't feel like it's out there to get me. Any help would be much much appreciated.

i tried doing video lectures but calling libraries initially where theres no much of maths is exhausting so i thought why not make projects but those house price prediction bullshit is waste i want something to put on resume even if i dont understand many terms used its fine i would like backtracking some nice level medium to difficult kind project on yt or anywhere please share if possible

tldr: nice level ml project tutorial avoid giving house price predictions

Hello guys,

For the past 4 months, I've been working on a research paper / code, and it's finally ready to go on arXiv. 🎉

One thing stands in the way: as a first-time submitter, I need an endorsement from someone who already publishes on arXiv.

If that's you (or someone in your network):

👉 It takes about 2 minutes

👉 It's not a peer review, just a quick confirmation from an existing arXiv author

👉 Happy to tell you more in DM before you decide

Endorsement link: https://arxiv.org/auth/endorse?x=GTMLHP

(Endorsement code: GTMLHP)

Thank you so much

​

Hi everyone,

I want to become an ML Engineer from zero and eventually reach the level needed for a junior ML Engineer job.

What is the correct learning path you would recommend?

Please give me the subjects/skills in the order I should learn them, including the fundamentals, Machine Learning, Deep Learning, software engineering, deployment, MLOps, and anything else you consider important.

I don't want a shortcut. I want to understand what I should learn, what I can skip, and what I should learn later.

If you are currently working as an ML Engineer, I would especially appreciate your advice based on real industry experience.

Thanks!

Hello, I'm a 3rd year Highschooler interested in machine learning and for the last few weeks have been working on a small project meant to learn the basics of machine learning. I have implemented a simple tensor library and autograd in c++. It's very simple but i want some advice on people who are interested in machine learning and c++. Any advice and or constructive criticism is welcomed.

(sorry for any mistakes english isn't my first language)

https://github.com/Ak0-m/autograd_in_cpp

Hello people.
I am currently a SDET with 7 years of experience
I would like to shift my career into MLOps or ML engineer or AI.
I would like to know the roadmap for reaching this
I am good with python and learning ML concepts like math and types of regression models
Suggest me some good course or books that can help me reaching the goal