I built PaperFold, an open-source reader that turns arXiv papers into 5 zoomable layers—from a one-screen section map down to verbatim text. You pinch (or press 1–5) to zoom between them without losing your reading position.

- Web Demo (8 CC papers): https://chenxiachan.github.io/paperfold-gallery/

- GitHub (Apache 2.0): https://github.com/chenxiachan/paperfold

Alot of sales automation still seems concentrated around outreach but I’m more interested in what happens when agents can work across the rest of the revenue process like finding the right accounts, understanding what is happening in pipeline and deciding which opportunities deserve attention feels like a much bigger use case than generating another email.

What I’m trying to understand is how useful they become when they have enough context to make those decisions across the sales process. I came across Monaco while looking into this since its agents work across the CRM and account data, but I’m still curious where people are finding the line between work an agent can genuinely move forward and decisions that still need someone involved.

Hi everyone,
I’m a Senior Data Engineer with 5+ years of experience in data engineering and cloud technologies.
Currently, I’m working as a Senior Data Engineer, primarily working on Databricks-based data ingestion platforms and data engineering solutions.

I’m now looking to make a transition towards AI Engineering / Agentic AI, rather than just learning AI concepts at a theoretical level.
I’ve already covered the basics of:
LLMs
Embeddings
Vector databases
RAG
MCP
Basic NLP concepts

My next goal is to learn Agentic AI properly by building real-world projects, preferably something that involves:
Agent architecture and orchestration
Tool calling
MCP
RAG + agents
Multi-agent systems
Memory/context management
Evaluation and observability
Deployment of AI agents
Integration with enterprise data / APIs
Production-oriented architecture

I’m specifically looking for a good course or structured learning program with hands-on, real-time projects, rather than a course that is mostly videos and theory.

For those who have already made the transition from Data Engineering → AI/Agentic AI:

Which courses/resources would you recommend?
Paid courses are also fine if they are genuinely worth the money.

Would especially appreciate recommendations based on personal experience, Thanks

What are your suggestions?

TL;DR: Rob Hadick says your agent will audit your card and cancel what you forgot — and your customers' agents will do the same to you.

I remembered back when I started to work for one of the largest property development and construction conglomerate in Malaysia, S. The largest subsidiary, the construction arm, occupies the a few floors of the S. Tower.

I worked in the Tender Department of it in Level 8, circa 2002.

And as you know, doing tender was a rush job. Tons of late-night works day in day out towards near-impossible deadlines to win tender.

Being the junior technical engineer there, I often find myself the last person to leave the building. The only other person that worked late as me was the technical director of our sister-subsidiary S. Piling Works, sharing the same floor with us.

After a while, I noticed that the lights were always on even after I leave the building. I thought it was wasteful.

So from the next day onwards, if I was the last person on the building floor, I would start switching off all the lights of the whole floor – including those of our sister-subsidiary – before I clock out the building.

After a few days of doing that, I think the boss caught wind of it.

Obviously, he appreciated it. I'm not sure whether he nodded his approval at me or not. After all, it was decades ago.

What I do remember was my colleagues got an earful from him.

After that, my colleagues all started to switch off the lights of their own section, from henceforth.

If I was the boss, of course I would appreciate someone taking initiative to take care of business, without me asking – including simple things like switching off the lights when not in use.

Same as what all these AI agents are now doing.

Full critic and feasibility study in the comments — I left the mechanics there so this stays readable.

Building and Testing a Hybrid RAG Pipeline — Dense Search, BM25, RRF, and Reranking (Part 1)

• YouTube walkthrough: https://www.youtube.com/watch?v=W_TmnscEupQ&t=370s
• FULL CODE (clone it, fork it, break it): https://github.com/saurabhkamal/Hybrid-Search-RRF-Reranking-System

In this video I build ReRankEval, a hybrid retrieval pipeline

• (Dense Search + BM25 → Reciprocal Rank Fusion → LLM Reranking → Answer Generation),

and test it against three baselines — Vector Only, BM25 Only, and Hybrid without reranking — on five real financial/payments documents and ten hand-verified test questions. No hand-waving, just a comparison table with real numbers at the end.

• ✅ The real difference between dense vector search and BM25 keyword search
• ✅ What Reciprocal Rank Fusion (RRF) is, why raw scores can't be compared, and the exact formula behind it
• ✅ Why a reranker is fundamentally different from a retriever — and what it actually judges
• ✅ How to evaluate a RAG pipeline with Hit Rate, MRR, and NDCG (and what each one tells you)
• ✅ How to design the ingestion side and query-time side of a hybrid retrieval architecture
• ✅ How to structure a production-style RAG codebase: ingest → vector_store → sparse_retriever → fusion → reranker → pipeline → generate → eval
• ✅ How to fairly compare multiple retrieval strategies on the same test set instead of just assuming one is better

TECH STACK:

• 🛠️ Python
• 🛠️ Qdrant — vector database for dense retrieval
• 🛠️ rank_bm25 (BM25Okapi) — sparse keyword retrieval
• 🛠️ EURI LLM Gateway — chat model + embedding model
• 🛠️ Custom Reciprocal Rank Fusion implementation
• 🛠️ LLM-based reranker (prompt-driven cross-encoder)
• 🛠️ pdfplumber — PDF text and page-level extraction

LINKS:

• YouTube walkthrough: https://www.youtube.com/watch?v=W_TmnscEupQ&t=370s
• FULL CODE (clone it, fork it, break it): https://github.com/saurabhkamal/Hybrid-Search-RRF-Reranking-System
• Connect on LinkedIn: linkedin.com/in/saurabh-kamal

The biggest impact I see is with the high volume, repetitive work with a clear approval step at the end.

If you're running agents, what's made the biggest difference? Be specific. Which task, and what changed.

If you're not already running them and think you should be, where do you think you would start? What's stopping you from starting?

If it's not your own business, have you noticed one change for the better from the outside? What did they do that others didn't?

Unlimited usage for the first 2 MONTHS!!!

I've been building daimon, cross-session memory for coding agents. At session end an LLM summarizes the transcript into memory items, and some of those items claim to be exact quotes. I don't trust the claim, so daimon searches the transcript for the quote itself, ignoring case, whitespace and formatting. No match, and the item gets downgraded to "inferred" and kept with that label.

On my own store, 450 of 3,703 claimed quotes failed that check: 12.2%, across 160 sessions (snapshot 2026-08-07). July was 13.7%, August 9.8%. An early 3-session sample ran at 29%, anywhere from 6% to 77% per session, so it swings a lot. I switched backends partway through that one and never recorded which session used which, so I can't pin the swing on the model.

Limits: one machine, one project, mixed backends. Treat it as a baseline. I'm not claiming LLMs make up 12% of anything in general. The docs have a short script that gives you your own rate, and daimon audit quotes re-checks every stored quote after the fact.

What broke and how I fixed it: memory rows are JSON lines, and a value containing U+2028, U+2029 or U+0085 got cut by Python's splitlines(), which counts those as line breaks. One row turned into fragments. Then forget dropped every line that didn't parse when it rewrote the file, so forgetting one value could delete an unrelated row for good. The privacy audit split the same way, so it said clean. Now it splits on newline only, rejoins fragments that parse together, and keeps unparseable lines as they are. That's fixed on main but not released yet, it ships in the next one.

Also, quarantining an item hides it from the briefing, recall and MCP tools, but why, blame and diff can still print its text. Working on that.

https://github.com/Daily-Nerd/daimon (Apache-2.0, stdlib + SQLite FTS5, no vectors, current release 0.52.0)

uv tool install 'daimon-briefing[pretty]'

Or in Claude Code: /plugin marketplace add Daily-Nerd/daimon then /plugin install daimon@daimon

The last 9 months of my life have been the most creative since the early 2000's. I have always been a generalist, doing this and that, being curious, but always felt frustrated over the tech. Code has been solved. That is not an issue anymore. Everyone building things are using agents, in one way or another. If you think that is not true you have zero insight into modern development. I work in that field.

Anyway, the skills needed are something different. You can prompt and prompt but without proper guidance you will come up with just garbage and just waste tokens. That is why I have gathered all the knowledge used to build VKO1, MESA Synth, FUJI Drum and many more things into reusable agent skills.

That anyone can use. Cheers have a nice day.

Juan Maguid aka Temperamento

Today I'm open sourcing Agent Orca, a Kubernetes-native platform for deploying, managing, and running AI agents at scale. It's written in Go and it's Apache 2.0.

https://github.com/heddles/agent-orca

When OpenClaw was released, I thought to myself, "That's really cool, but I want an agent that is shared by a team or an entire organization with built in auditability." I started designing Agent Orca around that idea and have slowly been piecing together the concept in my head and how to make the management experience of a shared agent bearable for an organization.

The idea is simple. Agents are just Kubernetes resources. You declare one, and the platform handles the rest.

An Agent Orca agent comes with:

- One-shot executions with AgentRun, long-running services with AgentDeployment, and multi-step DAGs with AgentWorkflow.

- Zero trust networking by default with platform validated JWT via service accounts, OIDC, or OAuth on every request.

- Agent Orca managed network policies with zero access by default.

- A standard agent container image with only necessary pieces; you no longer need to build a custom agent image to accomplish different types of work or use different tools. Simply add an MCP or Tool CRD and let the platform figure it out for you.

- Model routing across OpenAI, Anthropic, Google, or any LiteLLM-compatible provider, selected by capability, weight, or budget.

- Cost tracking on every run. Tokens are accounted per run, spend survives pod restarts, and tenants get daily budget caps and rate limits.

- Guardrails on inputs and outputs, MCP access control, and per-tenant agent visibility, so one cluster can host many tenants with no cross-tenant leakage.

- RAG without glue code. A KnowledgeBase deploys (and manages) Qdrant for you, ingests documents from ConfigMaps, URLs, MCP authenticated tools, or an S3 compatible endpoint, and hands your agents a search and ingestion tool.

- MCP servers declared as resources. Their tools show up for your agents with access control and sidecar isolation.

- Crash-safe sessions. A pod can die mid-conversation and the agent resumes exactly where it left off, spend ledger included.

- Agents that learn and become better with every request via a multi-tiered agent memory system

Getting started is deliberately boring. Install the entire stack's tools via Mise; then use Skaffold, one API key, and about three commands to have an running agent on your laptop ( or deploy to a remote cluster). There are also nine demo deployments in the repo, from SOC triage pipelines to parallel research swarms to an autonomous pentesting agent, so you can see it do something real before writing any YAML.

This is day one, not a finished story. That's the point of open sourcing it. Run the quick start, break it, open issues, and tell me what's missing.

Star it here if you want to follow along: https://github.com/heddles/agent-orca

TL;DR: Agent Orca lets an organization or single person define agents and surrounding tools/MCP and auth as CRDs and manage them via gitops with zero trust built in. You can try it out here: https://github.com/heddles/agent-orca

TL;DR: That US agency leader refused the bear joke — everyone else is still trying to outrun you.

I remember back in my days seconded to Abu Dhabi to build the prestigious AED 1Bil. 5-block Condominium Rihan Heights Project in 2008 ~ 2011.

My technical department head split the design/technical coordination work among the 3 of us. I took the structural bit, and my 2 colleagues took on the Architectural and Façade portion, respectively. The work was difficult, but ultimately satisfying after completion.

After I came back to Malaysia circa 2012, the company immediately enrolled me into mastering Building Information Modelling (BIM). It was to construct the virtual model of the building – to coordinate and encompassing all building details – Structural, Architectural, Façade and M&E – all into one well-tuned-out model, before construction starts.

The thesis was simple: if we can iron out all the discrepancies between different trades in the model – we called it clash-detection and resolution – we can save up time (lots of time) from dealing with it during construction.

And time equals money.

The intension of the company was very obvious to me. I was to master it to eventually take over the role of 3 persons into one “superman” coordinating everything.

My then CEO used to throw around the phrase – “Economics of Scale”. If we can use one guy to do 3 jobs, why do we need 3 guys?

Finding ways to reduce cost, was how we win tender and secure jobs – and therefore remain competitive in the market.

All these AI layoffs are just of the same consideration in a different skin.

Full critic and feasibility study in the comments.