We spent months optimizing engineering speed. Deployments improved. Users barely noticed. What moved engagement instead was fixing one confusing workflow. What's something your team optimized that users never cared about?
Building software is becoming easy.Building a business is not.AI coding tools have dramatically lowered the barrier to creating SaaS products, AI applications, online services, marketplaces, and e-commerce stores. Entrepreneurs can now generate working products in hours instead of spending months writing code.But for most founders, development is no longer the bottleneck.Launching a successful business still requires validating demand, researching competitors, defining positioning, creating mark
Agents write code really well, but it can get hard to understand what they've written and why. Shikhu is a CLI tool and Agent Skill that facilitates learning your code through self-quizzing, transcript analysis, and validation flows.I'm Arjun, I'm currently a developer advocate at Pinecone, and I use agents to code a lot!I enjoy using agents to code, but I've been feeling like I've lost some conceptual learning and understanding that comes with writing the code yourself.
I built a remote desktop app where your FIDO2 security key acts as the address. The hmac-secret produces the same Iroh P2P node identity on both ends. That means there are no accounts, pairing, or VPN required.I documented an agentic development workflow retrospective, including PoC spikes, spec driven design, agentic roadbumps, and what "shipping v0.1" means to me. This writeup covers the agentic development process-changes I am making to adapt to various evolving DevEx tools; focusin
This year marks Meta’s 10th consecutive year as a sponsor of the Python Software Foundation (PSF), the charitable organization dedicated to advancing, supporting, and protecting the open-source Python programming language and the community that sustains it. Python is one of the world’s most influential programming languages, and we use it across our engineering stack, from thebackend of our apps and products like Instagram and Threads to cutting-edge AI research. We recognize the vit
There's a well-worn path to resumable AI chat streams: find the Vercel SDK docs, implement Redis-backed replay, and ship it. For many products, that's the right call.The challenge arises when the product goes further than that. AI customer support tools that handle complex queries over 30-plus seconds. Agents that keep working while the user switches from their laptop to their phone. Products deployed to enterprise customers whose networks terminate long-lived Server-Sent Events (SSE) connection
Caching In Distributed Systems Part : 1 Now, let's understand what caching is. Whenever your application needs data, it usually ...
In this post, we share our journey and the lessons learned from building and running a fully serverless, multi-account software as a service (SaaS) platform at scale. We’ll explore why true scale-to-zero is critical, how we handle quota management, why engaging AWS service teams early saved us from outages, and which unexpected practices emerged once we scaled from thousands to over a million functions. At ProGlove, we build smart wearable barcode scanning solutions that connect frontline worker
If you’re building machine learning solutions with sensitive data, you face a persistent challenge: preventing data exfiltration while enabling data scientists to work productively. iBusiness, an AI-driven fintech organization, needed its data scientists to work with sensitive data to fine-tune and improve machine learning models. As the data science team scaled, traditional air-gapped environments and monitored virtual desktops proved unsustainable, leading to high costs and operational complex
When your game server needs both a managed identity provider and its own session system, players face a broken experience if authentication forces a redirect or stalls gameplay. Dual-token authentication for Nakama game servers with Amazon Cognito solves this by connecting two independent session systems, each with its own token lifecycle, without interrupting the player. This post shows you how. Amazon Cognito handles player identity and Nakama manages game sessions. Cognito issues a JWT, a ser
<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/TNFM-header-light.max-600x600.format-webp.webp">Google Meet's "Take notes for me" feature is available to Google AI Pro and Ultra subscribers in select languages.
Authors: Lequn Wang, Jiangwei Pan, and Linas BaltrunasFigure 1. Autoregressive homepage generation. GenPage builds a Netflix homepage one row or entity at a time, each one conditioned on what’s already on the page and the user’s context.IntroductionThe Netflix homepage is the first thing users see when they open the app and the primary way they discover content to enjoy. Almost every part of it is personalized, including which rows appear, which entities show up within those rows, and how everyt
Hello HN,I would like to share with you linkedrecords.com - an open source backend as a service I'm working on since some time now. You can think of it as an firebase/convex alternative with an interesting twist.In 2018 I needed to write large software requirements/architecture documents in Google Docs. While I was annoyed by the limitations of Google Docs back then (no captions on figures, no automatic heading numbering, slow when docs are bigger,...) I was still fascinated by th
An earlier version of Databasus shipped a backup agent: a binary that ran on the database host to stream WAL and create physical backups locally. That first implementation turned out to be a mistake, and we removed it. Physical backups now run remotely from the Databasus host, as described above.Why the agent was the wrong approach:• It was a naive implementation that only copied WAL on top of full backups, which led to a long RTO• Users had to configure both Databasus and a separate agent, when
Hi, I'm a PhD student in Bioinformatics/Computational Biology with a software engineering background,I'm trying to pivot toward AI/ML research. I'm familiar with the practical side of AI as in using, Scikit learn, R, Pytorch, ONNX Runtime etc...I was thinking if LLMs could be used as a research assistant to create better AIML algorithms. So I asked ChatGPT to help find better way to solved one of the most computationally intensive problems in Transformer architecture ba
Hey HN,Thijs here! I'm the founder of Prototyper and today we're launching the first visual canvas built for agents.Couple of interesting lessons from building the product that I think are worth sharing:For the agents, everything is a file. In Prototyper, everything from plans, to apps, and diagrams, can be read as a file.We found that a filesystem is the most natural way for an agent to navigate: it discovers new content and functionality just by traversing the tree.We kept this layer
Modern CPUs e.g XEON 6 have 144 cores, 144 threads - which to me seems a heavy misalignment between the hardware engineers making the CPUs & the software engineers using the CPUs.on the software side - in distributed systems - most systems when containerized assume they're gonna be utilizing one CPU core in a horizontally distributed manner i.e many pods etc. the other pods could be on different machines.then the language platforms e.g JVM, Golang, JS are going the route of green thread
AI chats are often a rather solitary experience: just you and ChatGPT, sitting there together, solving a problem. But so many of the tasks that we perform day to day are ones that benefit from, or often even require, collaboration with other people such as colleagues, family members, or friends.So, if AI agents are helpful, and other people are helpful, then how can we provide a space for multiple people to collaborate with each other and with AI agents?This is a question to which the flagship A
AI adoption programs tend to end in the same place. Tools are accessible, usage is up, and there's a dedicated Slack channel for wins. Six months later, nothing about how the team works has fundamentally changed. People are doing the same things – just slightly faster. And it’s easy for programs to stall when you’re measuring the wrong thing. Adoption (whether people have access and whether they're using the tools) is visible and easy to report. It tells you nothing about whether the team i
Live polls are a staple of conferences, streams, and all-hands: a question goes up on the big screen, everyone votes from their phone, and the bars race each other in realtime.There's a lot of different ways you could implement this.The most obvious way is a CRUD app backed by a server that votes are POSTed to, and the server keeps a running count. But this is the Ably blog, so we're obviously going to use Ably Pub/Sub to build something which does not need each vote to go via your server. That