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This is the eighth and final post in a series on the false assumptions teams make when building with generative AI. The series began with the observation that the trough of disillusionment for AI-assisted development has arrived — not because AI is useless, but because eight false assumptions made the trough inevitable. This post covers the last assumption and closes the series. The Fallacy "If one AI agent gives us a 10x boost, ten agents will give us 100x." Why it's tempting The arithmetic feels irresistible. One agent generates code for the backend. Another generates the frontend. A third writes tests. A fourth handles database migrations. A fifth generates documentation. Each agent works in parallel. No meetings, waiting or coordination overhead. Pure throughput. Leadership sees the potential: a five-person team with fifty agents has the output of a fifty-person team at the cost of a five-person team plus API credits. The scaling is linear. The economics are transformational. And the early results confirm it. Each agent, working on its own, produces impressive output. The backend agent generates Go code. The frontend agent generates React components. The test agent generates test suites. Each agent, in isolation, looks like a 10x developer. Why it's wrong You've seen this problem before. It has a name. It's called distributed systems. A distributed system is a collection of independent actors that must coordinate to produce a coherent result. Each actor makes decisions locally. The system's correctness depends on those local decisions being compatible globally. When they aren't, you get inconsistency, conflicts, data corruption, and cascading failures. AI agents working on the same codebase are a distributed system. Each agent makes decisions — variable names, error handling strategies, retry policies, data formats, abstraction levels, dependency choices. Each decision is made locally, in the context of one prompt, one file, one task. No agent sees the full pict

Networking certifications like CompTIA Network+ and Cisco's CCNA are career-defining credentials. They validate your understanding of networking fundamentals — and both exams test OSI Model knowledge extensively. In fact, the OSI Model is arguably the single most tested conceptual framework in entry-level and intermediate networking certifications. Why OSI Is So Critical for Certification Exams Exam questions on OSI take many forms: "At which layer of the OSI model does a router operate?" (Layer 3) "What PDU is used at the Transport Layer?" (Segment) "Which protocol operates at the Application Layer?" (HTTP, DNS, SMTP...) "A user cannot connect to a website. Troubleshooting should begin at which OSI layer?" (Layer 1, then up) "Which device operates at Layer 2?" (Switch) "What is the function of the Presentation Layer?" (Translation, encryption, compression) These questions seem straightforward on paper but are notoriously confusing under exam pressure without deep conceptual understanding. How the OSI Simulator Accelerates Your Studies Visual Memory Formation Research in cognitive science consistently shows that visual and kinesthetic learning creates stronger memories than text-only reading. When you watch the OSI Simulator animate your message through all seven layers, you're forming episodic memories — vivid, experience-based memories that are far more durable than rote memorization. Protocol-to-Layer Association One of the most commonly missed exam categories is protocol-to-layer mapping. The OSI Simulator makes this automatic: when you select HTTP, the Application Layer is highlighted. When you watch TCP headers form, you associate TCP with Layer 4 viscerally, not just verbally. PDU Name Mastery Data, Segment, Packet, Frame, Bits — the five PDU names are shown explicitly at each layer in the simulator. After running 10 simulations, these names become second nature. No flashcard can match this experiential learning. Troubleshooting Framework Practice Network+ an

Hello, my name is Greg. About six months ago I started using AI chatbots like ChatGPT and Claude at work for small tasks — proofreading emails, summarizing meeting notes, that kind of thing. But also some technical stuff too. One thing that really came in handy was analyzing packet captures from Wireshark. I work with VoIP phone systems, and when things go wrong, feeding a PCAP file into an AI chatbot speeds up the troubleshooting process dramatically. Before long I was asking AI to write code. First simple HTML pages, then Python, then C#. I was amazed by the results. These weren't big projects — just small experiments — but they came to life in minutes instead of days. I found out there was already a term for this: vibe coding . Perfect, I thought. I made project after project and wanted to share the excitement with other people who were surely doing the same thing. I created a free learning website, published a book on Kindle Unlimited, and went looking for a community. I landed on Reddit. There were already vibe coding subreddits. I thought — this is great, I've found my people. Then reality hit. These communities had "vibe coding" in the name, but they weren't exactly vibe coding friendly. The term had already been claimed by people focused on monetizing their creations fast, with little interest in actually learning to code. That created a massive anti-vibe-coding crowd on the other side, and honestly there was an all-out war going on between them. Not really the place for someone just looking to share cool stuff they made. I came to a realization: I wasn't really a vibe coder — at least not the kind people were arguing about. I wasn't in it for the money. I was in it for the fun. I didn't mind learning programming concepts along the way. I wasn't trying to sell anything or launch a startup. I just liked making things and solving problems. So I retreated and regrouped. That's when I found a better description: Play-First Programmers . People who start by playi

We are exploring an experimental blockchain mechanism called "Proof of Weather" In the world of blockchain, various methods are used to achieve network consensus. The most well-known is Bitcoin’s Proof of Work (PoW). While PoW is an excellent mechanism, it has one major drawback. It consumes an enormous amount of electricity. At one point, I found myself wondering: Does blockchain really require such vast computational resources? Isn’t there something else that’s needed? This led to the creation of Dawn, the experimental cryptocurrency project I am developing, and an experimental blockchain mechanism called Proof of Weather. In this article, I will discuss: Why I decided to use weather How Proof of Weather works Security considerations Implementation in Rust How Does Proof of Work Work? Proof of Work is often explained as a mechanism where computers compete against each other in computational tasks. However, one important property of PoW is that it produces outcomes that are difficult to predict in advance. Miners repeatedly perform massive amounts of hash calculations, and only those who happen to meet the conditions can generate a block. This unpredictability plays a role in determining who can produce the next block. However, this process consumes enormous amounts of electricity worldwide. So I wondered: Aren’t there already phenomena in nature that are difficult to predict? Why Weather? Proof of Weather utilizes weather data as that unpredictable element. Of course, weather forecasts exist. However, Temperatures several days in the future Atmospheric pressure at specific locations Precipitation Wind speed and other factors cannot be predicted with absolute certainty. In particular, when combining observations from multiple locations, it becomes even more difficult to accurately calculate future values in advance. In other words, meteorological observations have the potential to be used as A real-world information source where future values cannot be fully predic

Getting started with the OSI Model Simulator takes less than 60 seconds. The interface is thoughtfully designed to be intuitive for beginners while offering enough depth to satisfy advanced learners. Here's your complete step-by-step guide. Step 1: Open the Simulator Navigate to app.osi-model-simulator.roboticela.com in any modern web browser. No account required, no download necessary, and no cost. The app loads instantly and is ready to use immediately. Alternatively, visit the landing page to learn more about features and download the desktop app for offline use. Step 2: Enter Your Message In the message input field, type any text you like. This is the "data" your simulation will encapsulate. Examples: Hello, World! GET /index.html HTTP/1.1 {"user": "alice", "action": "login"} Your own name or a phrase you'll remember Using a personally meaningful message makes the encapsulation feel real rather than abstract. Step 3: Choose Your Protocol Select from five real protocols: HTTP, HTTPS, SMTP, DNS, or FTP. Each choice changes the Application Layer headers added to your data. For beginners, start with HTTP. Then re-run with HTTPS to see the Presentation Layer encryption difference. Step 4: Choose Your Transmission Medium Select your Physical Layer medium: Ethernet, Wi-Fi, Fiber Optic, Coaxial, or Radio. This affects how the Physical Layer is visualized at the end of the simulation. Step 5 (Optional): Set Custom IP Addresses For a more realistic Network Layer demonstration, enter a source IP address (simulating your device) and a destination IP address (simulating the server). This makes the Layer 3 packet header concrete and personally relevant. Step 6: Run the Simulati on Click the Run or Start button. Watch as your message travels through all seven layers: Application Layer adds protocol headers Presentation Layer adds encryption (if HTTPS) Session Layer adds session management Transport Layer segments and adds TCP/UDP header Network Layer wraps in IP packet Data Li

The Physical Layer's choice of transmission medium profoundly affects the performance, cost, security, and reliability of a network. The OSI Model Simulator supports all five major media types — making it a powerful tool for understanding how physical choices ripple up through all seven OSI layers. Medium Speed Max Distance Security Cost Ethernet Up to 10 Gbps+ 100m (Cat6a) High (physical access) Low Wi-Fi Up to ~9.6 Gbps (Wi-Fi 6) ~100m indoor Medium (WPA3) Low Fiber Optic Terabits/s 100s of km Very High High Coaxial Up to 1 Gbps 500m (RG-8) Medium Medium Radio Variable (5G: Gbps) km to global (satellite) Low–Medium Variable Ethernet: The Reliable Standard Ethernet is the dominant wired networking standard in homes, offices, and data centers. Using twisted-pair copper cables (Cat5e, Cat6, Cat6a), it provides reliable, high-speed connectivity with predictable latency. The IEEE 802.3 standard governs Ethernet, and modern variants include 1GbE, 10GbE, 25GbE, 40GbE, and 100GbE. Wi-Fi: Wireless Freedom Wi-Fi (IEEE 802.11) eliminated the need for physical cables in most consumer settings. Wi-Fi 6 (802.11ax) and Wi-Fi 6E deliver impressive speeds, but shared medium access, interference, and radio propagation challenges mean it will never fully replace wired Ethernet for critical applications. Fiber Optic: The Internet's Backbone Fiber optic cables carry data as pulses of light through glass or plastic strands. They're immune to electromagnetic interference, support enormous bandwidth, and can span continents — literally. Every major internet exchange, submarine cable, and data center interconnect uses fiber. Coaxial Cable: The Cable TV Legacy Coaxial cable — familiar from cable TV connections — consists of a central conductor surrounded by insulating layers and a braided metal shield. DOCSIS-based cable internet connections (common from ISPs like Comcast) use coaxial as the last-mile medium. Radio: Wireless at Scale From the cellular 5G network in your pocket to satellite

It's important to monitor Dataform - jobs executed by Dataform can be the primary source of BigQuery costs in a modern data platform. Forgetting to incrementalise a table, using a table instead of a view in the wrong place or performing complex window functions on a large table can all incur large costs and long run times. Using the WorkflowInvocationAction for each job we can extract its BigQuery Job ID, then extract key metadata for each BigQuery job by querying INFORMATION_SCHEMA.JOBS_BY_PROJECT , before writing the output back to BigQuery so that it can be analysed (maybe even by transforming it in Dataform). from google.cloud import dataform_v1 from google.cloud import bigquery from datetime import datetime # ------------------------------------------------------------ # CONFIG # ------------------------------------------------------------ PROJECT_ID = " my-project " REGION = " europe-west2 " REPOSITORY_ID = " analytics " WORKFLOW_INVOCATION_ID = " 123456789 " BQ_REGION = " region-europe-west2 " OUTPUT_TABLE = " my-project.raw_dataform_monitoring.raw_dataform_bigquery_metrics " # ------------------------------------------------------------ # CLIENTS # ------------------------------------------------------------ dataform = dataform_v1 . DataformClient () bq = bigquery . Client ( project = PROJECT_ID ) repository = dataform . repository_path ( PROJECT_ID , REGION , REPOSITORY_ID ) invocation_name = f " { repository } /workflowInvocations/ { WORKFLOW_INVOCATION_ID } " # ------------------------------------------------------------ # 1. GET WORKFLOW INVOCATION ACTIONS → EXTRACT JOB IDS # ------------------------------------------------------------ job_ids = set () actions = dataform . list_workflow_invocation_actions ( parent = invocation_name ) for action in actions : # only BigQuery actions contain job metadata if hasattr ( action , " bigquery_action " ) and action . bigquery_action : if action . bigquery_action . job_id : job_ids . add ( action . bigquery_action

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Spent the weekend building a local tokenizer to stop leaking DB passwords and API keys to ChatGPT, literally can't stop testing edge cases. Written in pure TypeScript. Uses greedy reverse anchoring to mask credentials locally in the browser. Provided the core sanitizer logic here: https://github.com/abests/ghost-sanitizer-js submitted by /u/zero_backend_bro [link] [留言]

Hi everyone, I’ve always felt frustrated by how many ads are crammed into most tier list websites. That’s why I created a brand new site with absolutely no ads. I also made sure it's highly mobile-optimized and offers multiple downloading options for your tier lists! I'm always looking to improve, so please feel free to share any feedback or features you'd like to see added. ps. As a thank you for checking it out early, if you create 1 tier list template now, I will upgrade you to a lifetime Premium account. Even if ads are introduced later down the road, your account will remain completely ad-free. submitted by /u/Mountain-Shoe-6070 [link] [留言]

Article URL: https://www.ycombinator.com/companies/mbodi-ai/jobs/WYAcNkX-founding-machine-learning-engineer Comments URL: https://news.ycombinator.com/item?id=48424103 Points: 0 # Comments: 0

Apple has been on its back foot, AI-wise, for the past few years. But in a strange way, playing from behind might not be such a bad move. At WWDC on Monday, Apple appears to be getting ready to reintroduce us to the new Siri. Again. As a reminder, we met the new Siri in […]

if youve been on the internet long enought you probably know vommitedthoughts a person that created the mimic irl and he can talk to it and it replies very human like, so ive been wanting to make my own chatbot like that called kira but idk how my last experience with python chatbots failed since it was SO dumb and it started talking to itself so how do i make my own chatbot that i can constimize its personality ?? submitted by /u/i_am_X-Kira [link] [留言]

The AI workspace that plans, designs and ships with you. Discussion | Link

I was working with websockets and wanted some lightweight solution which was easy to work with and I never liked working with plain websockets and as an experiment I started building a typesafe solution and which is how I came up with @frsty/wsrpc . You can define procedures on the server (like trpc) , get typed send and on on the client. No codegen, no event codes objects to share between client and server, 0 runtime dependencies. Handlers are generator functions, yield events, return the RPC response: https://preview.redd.it/0igrus569n5h1.png?width=3680&format=png&auto=webp&s=880c6b2fd90a04866058bc91d703995c14482b07 All the event codes, returns and callback functions are typesafe. Works with zod, valibot, arktype, anything that implements Standard Schema. framework-agnostic. Still early so would appreciate some feedback. For detailed example see github.com/frstycodes/wsrpc submitted by /u/frstyyy [link] [留言]

I tried all the top models to find the best 3-in-1 Apple charging stations, pads, and more. Keep your iPhone, Apple Watch, and AirPods topped up with these WIRED-tested docking systems.

If you’re a full-stack engineer or technical architect willing to learn production-grade enterprise agents, you need architecture, security, and type-safe systems. That’s why we built AgentSwarms.fyi —the ultimate hands-on educational platform for teaching agentic AI and multi-agent workflows. 🚀 The Core AgentSwarms Ecosystem: Real-World Architectures: Skip the generic hello-world loops. Learn production-grade systems like human-in-the-loop validation, automated multi-platform content multiplexers, and secure code-sandbox environments. Deterministic Cloud Guardrails: Deep dives into multi-cloud token economics, dynamic cost-optimized routing, and model evaluation metrics. Grassroots Engineering Focus: No corporate marketing fluff. Just raw, practical code patterns designed to bridge the gap between fragile prototypes and stable cloud deployments. 💣 The New Drop: 60+ Browser-Native TypeScript Notebooks We just completely re-engineered our learning workspace. We’ve added 60+ fully interactive TypeScript Notebooks running 100% natively in your browser. No pip install dependency hell, no local Docker setup, and zero environment friction. Read the architecture, tweak the system prompts or Zod schemas, hit play, and watch the streaming terminal execute live across the five absolute best frameworks in the ecosystem: 🟢 LangChain.js (Fundamentals & Middleware Guardrails) 🔀 LangGraph.js (Cyclic Graphs & Stateful Orchestration) 💾 LlamaIndex.ts (Sentence-Window Retrieval & RAG Triad Evals) ⚡ Vercel AI SDK (Streaming UI Integration) 🤖 OpenAI Agents SDK (Lightweight, low-boilerplate loops) Stop passively scrolling through video courses. Open a canvas, break the graph nodes, and start compiling real multi-agent swarms. 👉 Dive in for free: agentswarms.fyi/learn submitted by /u/Outside-Risk-8912 [link] [留言]