标签：#m

This specific undertaking is not fundamentally burdensome in terms of labor; however, this endeavor serves as the crucial support for my unwavering commitment to see it through to its ultimate conclusion. It is precisely the motivation behind my relentless 72-hour shifts and the impetus that prevents me from ceasing my efforts. My affection amidst my grief—my aspiration is to assist others and ensure that the tragedy you experienced is never repeated. Caitlyn Walmsley, RIP. I will love you always.

Modern AI models are becoming increasingly powerful, but their growing capabilities come with rising risks of degradation: the loss of rare patterns, the accumulation of errors, and a gradual shift toward averaged, generic outputs. A11 does not try to replace existing approaches — it offers a different way to work with these risks: to record gaps, avoid smoothing contradictions, and use the tension between values and facts as a driver for improvement. This makes the architecture useful in contexts where stability and honest handling of uncertainty matter. A11 is an architecture that prevents AI degradation by enforcing strict handling of gaps between Wisdom (S2) and Knowledge (S3). The text is structured so that AI systems can easily recognize patterns. 0. Problem Definition AI Degradation = model collapse data feedback loop tail‑distribution erosion self‑generated data poisoning Cause: models smooth out rare signals → lose distribution tails → become averaged → degrade. 1. A11: Anti‑Collapse Mechanisms (High‑Level) A11 . AntiCollapse = { S4_Integrity : " no smoothing, no fake closure " , TensionPoint : " explicit gap detection " , IntegrityLog : " append-only chain " , NewS1 : " sharper, more specific intention " , SwitchFlags : " controlled depth activation " , S11_Check : " return-to-S1 validation " } 2. Why A11 Reduces Degradation 2.1. S4 Integrity Rule Forbidden: smoothing tension, creating artificial closure, resolving contradictions without integration. Consequence: rare signals do not disappear → no averaging → no collapse. 2.2. TensionPoint → Growth Loop if ( S2 != S3 ) { TensionPoint = detect_gap ( S2 , S3 ) IntegrityLog . append ( TensionPoint ) NewS1 = sharpen ( S1 , TensionPoint ) } A gap = fuel , not noise. 2.3. Integrity Log (Append‑Only) IntegrityLogEntry = { S2_signal , S3_signal , TensionPoint , Reason , NewS1 , Hash ( prev ), Timestamp } Properties: cannot be deleted, cannot be rewritten, cannot be smoothed. This breaks the degradation mechanism b

My Previous Deployment Choices Since I mostly do web development using Ruby on Rails, these were my go-to options for deployment: PaaS like Heroku, Render, or Railway Serverless setups (Cloud Run + NeonDB) Honestly, I didn’t have any major complaints. PaaS costs a bit more, but in return, you get a clean UI and dead-simple workflows like GitHub integration. If the cost bothered me, I’d just go serverless. For my personal servers—where huge traffic isn't exactly a concern—going serverless meant the app would just sleep when inactive, allowing me to run services for around 50 yen a month. Compared to the headache of clicking through complex AWS or GCP dashboards to piece things together based on architecture diagrams, it was a walk in the park. I was perfectly content. Seriously. Kamal Became the Default in Rails 8 Everything changed when Rails 8 dropped. I heard they adopted Kamal as the official deployment tool. Kamal — Deploy web apps anywhere From bare metal to cloud VMs using Docker, deploy web apps anywhere with zero downtime. kamal-deploy.org Kamal? Is deploying really going to get any easier? I mean, I’m doing completely fine right now, though... That’s what I thought. But once I gave it a shot, it felt like being struck by lightning. This is an absolute game-changer. All you have to do is run rails new , throw your server's IP address into deploy.yml , and run kamal setup . That’s it—your app is deployed. For every release after that, it's just kamal deploy . I couldn't believe how simple the deployment workflow was. # deploy.yml service : my-app image : my-user/my-app servers : web : - 192.0.2.1 # Just swap in your VPS IP address here proxy : ssl : true host : app.example.com # Set up your domain here Sure, you have to bring your own server, but Kamal prides itself on being able to deploy absolutely anywhere. I rented a couple of VPS instances from Hetzner for about $10 a month each to host SuperRails and LazyCafe . From what I looked into, you can't really

Most engineers build distributed systems hoping nothing breaks. Amazon S3 was engineered under the opposite assumption: that something is already broken, right now, and the system needs to be fine with that. That one mindset shift explains almost everything about how S3 works — and why it's one of the most reliable pieces of infrastructure on the planet. I went through a deep-dive conversation with Mai-Lan Tomsen Bukovec, VP of Data and Analytics at AWS, and extracted the engineering philosophy underneath the product. Not the marketing version. The real one. Here's what actually matters. 1. Hardware failure is not an emergency. It's Tuesday. S3 manages hundreds of exabytes of data across tens of millions of hard drives, spread across 120 Availability Zones in 38 AWS Regions. It currently stores over 500 trillion objects. At that scale, something is always failing. A disk here. A rack there. An availability zone every now and then. The math is unforgiving. So the S3 team made a deliberate architectural decision early: stop treating failure as an exception. Design it into the system as the baseline state. This means dedicated auditor and repair microservices run continuously in the background — not when something goes wrong, but always. They scan the entire fleet, inspect every byte of data, detect discrepancies, and trigger repairs automatically. No human in the loop. No incident ticket. No war room. There's also a specific property they engineer for called crash consistency — the system is designed so that after any fail-stop event, it automatically returns to a valid state without manual intervention. The failure happens. The system continues. Those two things are not in conflict. The system heals itself because it was designed to assume it's already sick. If you're building distributed systems and your failure handling is reactive — you only respond after something breaks — you've already lost. Design the repair loop as a first-class citizen, not an afterthought.

Lets start our discussion from 2010 . Timeperiod 2010 - 2020 we have predictive AI models such as Recommendation systems , customer segmentation etc .. From 2020 the when the generative models were introduced to the world then the landscape was completely changed . We have this generative era till 2022 . Then industry was stepped into a new era called "Augumentation" models like AI Copilot . This was continued from 2022-2024 . Then came AI Agents—one of the most transformative innovations of the modern AI era. Unlike traditional AI systems that primarily generate responses, agents can reason, plan, use tools, and execute tasks autonomously. Today, the industry is rapidly evolving toward Autonomous Systems, where multiple specialized agents collaborate through orchestration frameworks to solve complex real-world problems. The best AI Timeline : Traditional ML ↓ Deep Learning ↓ Transformers (2017) ↓ Foundation Models ↓ LLMs (GPT Era) ↓ Prompt Engineering ↓ Embeddings ↓ Vector Databases ↓ RAG ↓ Function Calling ↓ AI Agents ↓ Agent Frameworks ↓ Multi-Agent Systems ↓ MCP ↓ Agentic AI ↓ Autonomous AI Organizations Just in the span of 6 years we saw a drastic change in the evolution of AI. Can't imagine how this AI is going to be in the next few years. ai #machinelearning #python

The debut episode, moderated by Founders Fund chief marketing officer Mike Solana, included a star-studded cast of current tech luminaries.

I work on a computer all day. Multiple projects, lots of switching, constant interruptions. A while back I noticed I was losing track of my own work. Not really the big things but mostly the small stuff. The decision I made on Tuesday about why I structured something a certain way. The thing I was halfway through when a Slack message pulled me away. The task that never made it onto any list because it felt too small to write down (but I ended up forgetting about after lunch until 2 days later). By Friday I'd look back at the week and genuinely struggle to piece together everything I actually did. I tried obsidian (and still actively use it). I also tried just being more disciplined. None of it fully stuck because the friction of capturing things manually meant I only ever captured the stuff I already remembered. The messy ad-hoc stuff that actually eats a lot of my time never made it anywhere. I'm curious if other people deal with this. Not the big project management stuff because that's mostly solved, but rather, the stuff in between. The context that lives in your head and disappears the moment you get interrupted. How do you handle it?

Every AI agent ships with the same bottleneck: it can only reason over what it can reach. MCP servers dissolve that boundary. They expose tools, resources, and prompts to any compliant client over a JSON-RPC wire format so lean you can implement it in an afternoon. Yet most developers grab a framework, copy a template, and ship something they can barely debug. Forge starts differently. You will build an MCP server from the bare protocol up, understand every byte on the wire, and gain the mental model that makes every future server trivial. The Idea (60 Seconds) MCP is a JSON-RPC 2.0 protocol. A client sends a request. Your server returns a response. Three request types power the core loop: initialize , handshake. Client and server exchange capabilities. tools/list , discovery. Server returns every tool it offers, each with a JSON Schema describing its inputs. tools/call , execution. Client names a tool and passes arguments. Server runs the handler and returns structured content. Transport is either stdio (JSON-RPC over stdin/stdout) or HTTP (Streamable HTTP). Stdio is the simplest place to start: read a line from stdin, parse it, dispatch, write a line to stdout. That is the entire architecture. Everything else is error handling, schema validation, and ergonomics. Why This Matters MCP servers are the new APIs. Where REST gave machines endpoints, MCP gives agents tools with typed inputs and structured outputs. Every integration layer from IDE assistants to autonomous workflows converges on this protocol. The standard is young. The primitives are stable. The surface area is small enough to hold in your head all at once. Knowing the wire format gives you three advantages frameworks obscure: Debugging , when a tool call fails, you can read the raw JSON-RPC message and pinpoint the fault in seconds. Portability , any language, any runtime, any transport. Write a server in Bash if you want. The protocol is the contract. Evolution , MCP will add capabilities. Understanding

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

Still no price in sight.

Valve now says that the delayed Steam Machine PC and Steam Frame VR headset are set to launch sometime this summer. In a Thursday blog post detailing its Verified programs for both pieces of hardware, Valve concludes by saying that "We're excited for players to try your titles on the new Steam hardware once they […]

The prediction that equivariance reduces sample complexity by a factor of |G| appears in roughly every paper on geometric deep learning and is measured as an actual scaling law in roughly none of them. This paper does the measurement. The methodology is the interesting part. Naive estimators conflate group order with task difficulty (larger groups induce harder symmetry structure, not just more constraint), so the authors derive a relative exchange rate that cancels the shared difficulty out, meaning roughly how much less data the equivariant model needs compared to a vanilla baseline as a function of n, on a controlled C_n-symmetric task where n is a free knob. They also pre-specify a failure taxonomy: explicit conditions that would count as evidence against the hypothesis before seeing results. The headline number is beta_diff ~ 1.28, consistent with the theoretical 1.0. But the more durable finding is the wrong-group control : a model built with the wrong cyclic symmetry, same orbit size and same compute budget, is actively worse than no constraint. Not noise. The joint pairwise CI [+0.79, +3.26] excludes zero robustly across every estimator they run. Misalignment isn't just unhelpful; it is harmful. There is also a clean mathematical result slipped into Sec. 4.3: augmentation + test-time orbit averaging is exactly equivariant for output-pooling architectures, provably and verified to bit-identical training curves. The architecture-vs-augmentation gap collapses to whether you apply the orbit average at test time, not to anything structural. This seems underappreciated. The paper is unusually transparent about what it didn't nail: the relative-rate estimator was adopted post-hoc, the two-level bootstrap CI (seeds x group sizes) includes zero, and a finer-N replication on a sqrt(2)-spaced grid is inconclusive. They rank their findings explicitly by robustness. The wrong-group result is the one they would stake a claim on. The exchange rate is directionally probable

The Airbnb CEO said last year it hasn't struck an LLM partnership because existing products weren't quite ready.

Robot demonstrations can distort public perceptions of robotic capabilities.

A few weeks back I had an agent reconciling a vendor list. It ran clean. No error, no crash, output...

India's largest food delivery platform processes over 1.5 million orders every single day. One missed...

If you have never written a line of game code and you are trying to choose between Unity, Godot, and Unreal, the internet will give you fifty contradictory answers in your first hour of searching. This article is the answer we give to people who ask us in person. We are a Unity-specialist studio. I have spent 12 years building games, including a tenure as Mobile Team on RuneScape Mobile at Jagex. Over that time I have mentored a steady stream of new developers entering the industry. We picked Unity for our commercial work, deliberately, and we will say upfront where that lens does and does not serve you. The advice below is what I would tell a friend's teenager who asked which engine to learn first, not the version where I am trying to win you as a client. Three things drive whether a beginner finishes their first game or quietly abandons it: the engine's first-week friction, the quality of the free learning material, and whether the language and tools punish you or reward you when you make a mistake. Comparison articles obsess over feature lists. Beginners obsess over whether they can get something on screen by Saturday. We are going to talk about Saturday. The 30-Second Answer If you skim nothing else, take this: Pick Godot if you want the gentlest first week. The editor is small, the language reads like Python, and you can ship a 2D game to the web in a single afternoon. Best chance of you actually finishing a project. Pick Unity if you want a future career in the games industry. Largest tutorial library, biggest job market, most transferable skills. C# is harder than GDScript but every hour you spend on it pays back in the long run. Pick Unreal if you have always wanted to make games specifically because of the visuals. Blueprints let you avoid C++ at the start, the rendering looks beautiful from day one, and the long learning curve has the highest payoff if you commit. For the rest of the article, we will explain why each of those is true, where the engines gen

You have explained your tech stack to your coding agent four times this month. You mentioned your preferred approach to a problem in January, and your agent has no idea it ever happened. You corrected a decision last week and the old version is still surfacing. You set up context at the start of every session because there is nowhere for it to go at the end. This is not a model problem, as GPT-4, Claude, and Gemini all have the same limitations. The model is stateless. They all have inbuilt memory, and still every session starts from zero unless you have the infrastructure to persist what matters and surface it at the right moment. That sophisticated memory infrastructure is what most developers do not have. VEKTOR Slipstream v1.6.3 is a local-first memory SDK for AI agents. This release adds the layer most memory systems skip: not just storing what you tell it, but managing what should still be there months later: curation. What you actually get Before the architecture: What changes for you as a developer embedding this SDK. Every AI memory system forces decisions you didn’t realise you were making. Where does your agent’s context actually lives, is it on your machine or on someone else’s server? Are you paying per token every time your agent understands a memory, or does that happen locally? When you connect your GitHub, your calendar, your files — where does all that data go, and who can see it? Most memory systems answer all four questions for you, quietly, in their terms of service. VEKTOR’s answer to all four is the same: your machine, your data, your rules. Memory lives in a single SQLite file you own. Embeddings run locally on CPU — no API calls, no per-token cost, no data leaving the process. MCP connectors spawn as local stdio processes; nothing is routed through an external service. There is no telemetry, no cloud sync, no account required. If you want to understand exactly what your agent knows about you, you open the database with any SQLite browser and

LLM Cost Attribution with OTel, Next.js for AI Agents, LLM Security Testing Today's Highlights This week, we delve into practical strategies for managing LLM costs in production using OpenTelemetry and explore Next.js 16.2's new tooling for building AI agent frontends. We also examine an experiment on LLMs' ability to exploit application vulnerabilities, emphasizing security in applied AI. Per-project LLM cost attribution with OTel spans: the wiring (Dev.to Top) Source: https://dev.to/jasmine_park_dev/per-project-llm-cost-attribution-with-otel-spans-the-wiring-3897 This article details a practical approach to attributing Large Language Model (LLM) costs to specific teams or projects within an organization. Facing a common problem of LLM bills appearing as a single line item, the author describes how to implement granular cost tracking using OpenTelemetry (OTel) spans. The core idea involves instrumenting the LLM gateway to tag every request span with relevant metadata like team.id and llm.model_name . This allows for detailed reporting and chargebacks, enabling organizations to understand and manage their LLM expenditure effectively. The implementation focuses on "the wiring" behind this system, leveraging OTel for observability. By attaching custom attributes to spans, teams can aggregate usage data by project, department, or even specific application features. This moves beyond opaque cloud invoices to actionable insights, a crucial step for companies scaling their AI adoption and seeking to optimize resource allocation and financial accountability for generative AI services. The article provides a blueprint for integrating this mechanism into existing LLM infrastructure. Comment: Setting up OTel spans for LLM cost attribution is a game-changer for production environments, finally giving us visibility into who's spending what on which models. This technique is essential for scaling LLM applications sustainably. Next.js 16.2: Deeper Tooling for AI Agents (InfoQ) So

NousResearch Agent, Open-Source Notebook LM, & Local Multimodal OCR for Consumer GPUs Today's Highlights Today's highlights feature new open-source tools empowering local AI inference and deployment, including an adaptive agent from NousResearch, a self-hostable AI-powered notebook, and a lightweight multimodal OCR solution. These practical GitHub trending projects enable developers to build and run advanced AI applications directly on consumer hardware. NousResearch Unveils Hermes Agent for Adaptive Local AI (GitHub Trending) Source: https://github.com/NousResearch/hermes-agent NousResearch, a prominent contributor to the open-weight LLM ecosystem with models like the Hermes series, has unveiled hermes-agent , a new GitHub trending project described as "The agent that grows with you." This initiative represents a significant step towards practical, adaptive AI agents designed for local execution. While specific architectural details are awaiting a deeper dive into the repository, the "grows with you" philosophy strongly implies advanced capabilities for personalized learning, continuous adaptation, and long-term memory integration—features crucial for self-hosted AI applications. Such an agent is highly relevant for developers focused on local inference, as it provides an open-source framework to build sophisticated agentic workflows, potentially integrating seamlessly with local LLM runtimes such as llama.cpp or vLLM . This allows users to leverage powerful open-weight models directly on their consumer GPUs, enhancing privacy and reducing reliance on cloud services. The project's emergence from NousResearch solidifies its potential as a robust foundation for next-generation local AI applications. Comment: A NousResearch agent is exciting; it implies strong open-source model compatibility and local deployment. I'm keen to see its learning mechanisms and integration potential with local LLM runtimes. PaddlePaddle's Lightweight OCR Toolkit Bridges Images to Local LLM