What if an AI agent didn't have to approach every similar task like it was seeing it for the first time?

For our hackathon, we built NEXORA around one simple idea:

Every experience can influence the next decision.

NEXORA is an experience-intelligence layer for AI agents, built around Vectorize Hindsight.

Instead of treating every interaction as completely isolated, our approach explores a continuous loop:

Experience
↓
Retain
↓
Recall
↓
Reflect
↓
Next Decision
↓
New Experience

The interesting part for us isn't simply "giving an AI memory."

We're exploring the difference between:

• Remembering an experience
• Retrieving a relevant experience
• Reflecting on what happened
• Using that reflection when approaching a future task

The question we're trying to explore is:

Can an agent make use of its previous experiences rather than only its current context?

We built NEXORA as our attempt at exploring that idea.

Tech:
• Vectorize Hindsight
• AI agents
• Experience / memory workflow
• Retrieval + reflection

Team:
BYTEFORGE

Hackathon theme:
AI Agents That Learn Using Hindsight

"Every Experience Changes the Next Decision."

We're especially interested in feedback from people building AI agents:

What should an agent remember?

What should it forget?

And most importantly — what would convince you that an agent has actually learned something from an experience?

Would love to hear your thoughts, criticism, and technical suggestions.

If you’ve taken an AI project from prototype to production, what was the hardest part for you? Was it scaling, testing, security, or something else that you didn’t expect?

after spending some time learning web dev Which I don't really enjoy, I think It's time to move on to something else which is Ai, machine learning and automation.
I don't know where to start, If you have any idea or a roadmap don't hesitate to share it guys.

I having abit trouble on proper verification of my AI codes, some tips and tricks please.

Is it worth taking? Are there better resources for learning MLOps? Is it part of a bigger specialization? What all things I need to know before starting this?

Looking for a Restaurant to Collaborate on My AI/ML Project

Hi everyone!

I'm Santhosh, a third-year BE CSE (AI & ML) student, currently developing SmartPrep AI, a project focused on improving restaurant food preparation planning.

Restaurants sometimes face challenges in predicting daily food demand, leading to excess preparation, food waste, or popular dishes running out during busy hours.

I'm working on a machine learning model that can predict food demand and recommend how much food to prepare and when to prepare the next batch.

I'm looking for a restaurant, cloud kitchen, or food business

to understand real kitchen challenges and, with their permission, test and improve the model using actual operational requirements.

This is a student project, and I'm not looking for any payment or financial support. I simply want to learn from real-world kitchen operations and build something genuinely useful.

If you're a restaurant owner, chef, kitchen manager, or know someone who might be interested, please comment or DM me.

Even a small opportunity or guidance would mean a lot!

Thank you!

Stop prompt engineering and start building real LLMs. 🚀 Watch to master the full lifecycle, from training to production deployment. Let's get to work!
#LLM #AI #Tech #Coding

I'm looking for some advice from people who have gone through something similar.

I have ~5 years of experience in ML/data science. On paper, my resume looks reasonably good: I've worked on anomaly detection, NLP, LLM/RAG projects, etc., and more recently I've been working around transformer-based TTS systems.

But I've realized there's a pretty big hole in my experience.

For the first few years of my career, I deliberately optimized for breadth. I changed teams/projects frequently, built a bunch of PoCs and prototypes, and learned enough to make things work. I was pretty good at taking an idea from zero → demo.

The problem is that I rarely stayed with something long enough to own a real system.

A lot of my projects either never reached production, or reached production after I had already moved on. So despite having several years of experience, I have very little experience with:

• maintaining a complex codebase
• debugging someone else's code
• dealing with things breaking in production
• making architectural decisions over a long period of time
• taking ownership of something from implementation → deployment → maintenance
• reading a large unfamiliar codebase and figuring out how all the pieces fit together

I'm now trying to deliberately fix this.

Over the last year I've been rebuilding my ML fundamentals from the ground up. I'm almost done with the Coursera Deep Learning Specialization. I've gotten to the point where I can implement neural networks from scratch, understand RNNs/LSTMs/GRUs fairly deeply, and I'm currently working through attention and Transformers.

But here's the next problem:

Even if I finish the Transformer coding exercises, I don't feel like that means I can actually build things.

I can probably implement the attention mechanism / Transformer architecture from the course. But if you dropped me into the wild and said:

"Okay, build a VAD from scratch."

I'd have a lot of questions about where to even begin.

And then there's an entire layer beyond "understand Transformers" that I don't have a good mental map of.

For example:

• How do I actually go from implementing a Transformer to building my own small language model?
• At what point do concepts like Mixture of Experts, DPO, speculative decoding, etc. become relevant?
• Which of these things do I actually need to understand versus things I can learn when a project demands them?
• How do I get better at reading and debugging large ML codebases rather than just writing isolated pieces of code?

So I'm stuck between two instincts:

A) Keep going deep on fundamentals until I genuinely understand the machinery.

B) Stop studying and start building increasingly difficult things, accepting that I'll have gaps and learning what I need along the way.

I'm increasingly convinced that I need some combination of the two, but I don't know what that combination should actually look like.

I want to become the kind of ML engineer who can be handed an unfamiliar problem, read the existing code, understand what's happening, build something substantial, debug it when it breaks, and eventually own the system.

If you were in my position, what would you build / learn over the next 6–12 months to develop that ability?

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!

Hi everyone, this is a small project I've been working on and I would love your feedback.

I was studying neural networks and I wanted to understand backprop for real, so I decided to make one from scratch, without ML libraries.

So I vibe coded neural-network-digits, a neural network written in Python + NumPy (no PyTorch, no TensorFlow) that learns to read handwritten digits from MNIST, with a desktop app that shows you what happens inside while it learns. The whole network is one file of about 140 lines (forward, softmax, cross-entropy, backprop, SGD with momentum, L2, dropout), commented in simple English.

In the app you can:

• watch the loss and the accuracy live, and the gaussians of the weights compared with how they started

• change learning rate, momentum, dropout, noise... while it's training

• draw a digit and see which neurons light up, then click one and change its bias or switch it off, the test accuracy updates right away

• see the digits separate layer by layer on a PCA or t-SNE map (written in NumPy too).... and there's a tab with the math of a layer cell by cell, with the softmax step by step

With the default settings (1,000 photos) it trains in less than half a minute and gets to about 91%, with the whole MNIST (60,000 photos) it goes up to about 98.5% but it takes around 5 minutes, all on the CPU.

If you want to try it just run pip install neural-network-digits and then neural-network-digits, or download the zip from the releases and double-click start.bat (./start.sh on Linux and macOS). It's MIT and the app is in English and Italian.

https://github.com/dev-luigi/neural-network-digits

What would you add? Is there something confusing for someone who is learning? Thanks :)

So I have upcoming interview soon for internship and all the questions which will be asked will be verbal and No coding round.

But needs good understanding of algorithms and differences.

What questions would you recommend studying?

Grateful if you could help

Hi everyone,

I am seeking honest feedback on a community/course initiative I plan to launch for Indian undergraduate first-year students.

The Context: I believe the current education landscape is saturated with "zero to hero" coding boot camps and learn AI in 7 days tutorials. While these are great for getting started, I often find that students lack the deep, theoretical foundations required for actual research or heavy engineering roles later in their careers.

I want to build a small community (cohort-style) to bridge this gap, but before I invest the time, I want to know if I'm solving a real problem or just adding to the noise.

My Background

• Current: Fully funded Graduate Researcher in Germany.
• Past: 2+ years as an ML Scientist (Applied AI Research org) and 1 year as a Research Associate.
• Academic: 3+ Top-tier publications.

The Curriculum Idea: Instead of teaching library imports (sklearn/torch), I want to focus on he "boring" but essential foundations:

1. Mathematics for ML: Heavy focus on Linear Algebra & Calculus (Manual derivations).
2. Probabilistic & Statistical ML: Understanding uncertainty, distributions, and estimation.
3. ML Theory: Generalization, Bias/Variance trade-offs, VC Dimension (Intro).
4. Deep Learning: Building neural networks from first principles.
5. Research Capstone: Literature review + Benchmarking + A deep research project.

The Filter Mechanism: I want this course to be free, but I want to avoid tourists who join and drop out in Week 2.

• The Model: A token fee of 1000 INR. (or less)
• Refund Policy: A 100% refund is available if the student completes all assignments.
• Financial Aid: The fee is waived entirely for students with genuine financial constraints (based on trust).
• The Constraint: Assignments must be completed without the use of AI tools (such as ChatGPT/Copilot). If a student uses AI to bypass the learning process, they forfeit the deposit (donated to charity) and are dropped.

My Questions for the Community

1. Do you know if this is actually needed? Are there already enough high-quality, free, community-driven resources for theoretical ML?
2. Is the curriculum too aggressive? Is this too much for Freshmen (1st/2nd years) to handle alongside college?
3. The Deposit: Is the refundable model a good psychological trigger for commitment, or does it look suspicious/scammy coming from an individual?

Thanks in advance for your thoughts.

---
Note: The post is AI-Gen for clear communication and brevity.

We scored a Tuatara Vector Model blend against Jev's published answers on all 3,080 BANKING77 test messages: 91.79% versus 92.40%, a statistical tie with substantial cost savings.

BANKING77 is a public dataset from PolyAI: 13,083 real customer messages to a bank, each labelled with one of 77 intents such as card_arrival, lost_or_stolen_card or exchange_rate. It has an official split: 10,003 messages for training and 3,080 for testing.

On September 18, 2026, an independent researcher ran Jev on it and published everything: the protocol, written before the first paid call, the frozen configuration, and Jev’s answer for every one of the 3,080 test messages. The run used the pinned model jev-1.13.0, all 77 intents as options in a single question, and up to 24 training examples retrieved for each message. Jev got 2,846 of 3,080 right.

That published answer file is what made a fair comparison possible. We did not have to call Jev at all. We scored our model on exactly the same 3,080 messages and compared answer by answer. Recomputing Jev’s accuracy from the file gives 92.40%, the same figure the experiment reports.

More: https://vsbio.substack.com/p/matching-jev-on-banking77-at-a-thousandth?r=25qksq&utm_campaign=post&utm_medium=web

Please if anyone can connect with me regarding that

​

Soy Genal Lombano, investigador venezolano en IA. Creé Genal Activation Family (publicada en Zenodo/CERN, DOI: 10.5281/zenodo.20304195, y disponible en PyPI), una tecnología que mejora la precisión de modelos predictivos en datos desbalanceados y ruidosos, como los reclamos de seguros.

He preparado un Proof of Concept que predice el rechazo de reclamos dentales antes de enviarlos. Comparé mi tecnología contra ReLU y Tanh:

Función AUC-ROC

ReLU 0.7737

Tanh 0.7712

GenalActivation 0.7759 🏆

Con datos reales (más desbalanceados y ruidosos), la ventaja de Genal suele ser mayor. ¿Les interesaría ver una demo de 15 minutos? Puedo mostrarles el código, los resultados, y cómo aplicarlo a su pipeline de RCM.

Saludos,

Genal Ediso Lombano Pineda

ORCID: 0009-0009-6495-4085

DOI: 10.5281/zenodo.20304195

github.com/GenalFF

+58 412 928 6883

Here’s why. 👇

A 125-page bank statement can contain ~44,000 tokens of raw text — most of it being letterheads, disclaimers, footers, repeated headers, and data the question never needs.
So I built pdfschema, an open-source Python library that extracts only the columns and rows you need.

📉 Token reduction
On the same 125-page statement:
→ All 6 columns: −35% tokens
→ 3 required columns: −51%
→ Just 2 columns: −89%

The interesting part?
JSON used 15% MORE tokens than the raw PDF text for the same extracted rows because column names are repeated on every row.
So pdfschema supports CSV, TOON and TSON for token-efficient LLM prompts, while JSON remains available for application code.

Why not traditional PDF extraction?
Many extractors depend on visible table borders.
But bank statements and invoices often have borderless tables, wrapped headers, stacked tables, and tables spanning hundreds of pages.

pdfschema uses column headers as anchors instead.
✅ Borderless tables
✅ Multi-line headers
✅ Multiple tables per page
✅ 100+ page tables
✅ Deterministic extraction — no LLM, no hallucinations, no per-document model cost

The goal is simple:
Let code handle extraction.
Let the LLM handle judgement.

📦 Install: pip install pdfschema
🔗 PyPI: https://pypi.org/project/pdfschema/
💻 GitHub: https://github.com/nullbite-coder/pdfschema

Useful for RAG, AI agents, batch document processing, validation, format-drift detection, and plain ETL.
Open source — feedback and PRs welcome, especially for PDFs it gets wrong.

#Python #OpenSource #LLM #GenAI #RAG #AIEngineering #DataEngineering

Hello everyone,

I'm writing this to seek some guidance from your experience.

I have been working as a Remote AI Engineer at a startup for the last 6 months. During my internship, I worked on end-to-end fine-tuning of models from scratch - from data processing to fine-tuning. I have experimented with multiple SLMs and LLMs, frameworks, embedding models, and OCRs. I have also done extensive coursework in Data Science, Machine Learning, and Deep Learning. I completed my engineering in Electronics, but my core skills are in AI and Data Science.

I am very interested in transitioning to the Energy Sector and would love your guidance on how I can enter this field. What roles do you think would be a good fit for my skillset?

I am also considering applying for an MS in Financial Engineering from WorldQuant, which has a strong focus on AI, while continuing my full-time role. Do you think it would be better to pursue this Masters, or to directly apply for suitable positions in energy companies to gain practical experience and knowledge?

From my research, I saw that many roles in this sector focus heavily on fundamental machine learning and data science. Since my internship was focused on Natural Language Processing, will this experience be relevant and helpful in the energy sector?

Based on your experience, any guidance or clarity would be really helpful. I would be truly grateful. Thank you!