标签：#AR

I'm building EverQuill, an AI-powered tabletop RPG platform — a game where an AI acts as your dungeon master, narrating the story and reacting to what you do. Every turn of that story is a message to a language model, and every message costs money. That cost isn't a problem you get to solve later, once you're big. It's a set of choices you make before anyone shows up, because the defaults you pick decide whether a bad day costs you a few cents or a few hundred dollars. Here are three decisions I made before I opened the alpha, and why. 1. When the tier is unclear, I reach for the stronger model This is tier-based routing : which model serves a request depends on who's asking. Better models write better stories and cost more; cheaper ones are faster and rougher. So free players get Claude Haiku (cheaper, ~$0.80 per million input tokens) and paying players get Claude Sonnet (stronger, ~$3.00 per million) — plus, because the platform is designed to route across providers, the same decision could hand a turn to a local model running through Ollama instead of a hosted one. That part is straightforward. The interesting case is the ambiguous one. My routing hands over the cheaper model only when a request is clearly free-tier; everything else — paid, alpha testers, admin, anything the system can't cleanly place as free — gets the stronger model. The instinct most people have is the reverse: default to cheap, protect the margin, don't spend money you don't have to. The reasoning: a rough, disappointing experience for someone who should have gotten a good one is a real cost — it just doesn't show up on the bill. It shows up as someone deciding the product isn't worth paying for. Meanwhile the money risk is capped elsewhere (more on that below), so biasing toward quality can't run away from me. Given a bounded downside on money and an unbounded downside on reputation, I'd rather err toward quality than toward cheap. 2. I stopped paying full price for the part that never chang

From threats to the safety of poll workers to voters who can’t distinguish between odds and results, prediction markets are already scrambling the political process.

Jendrik Jördening shares practical engineering strategies for integrating LLMs into production pipelines. He discusses overcoming non-determinism, restricting schemas, separating semantic text extraction from deterministic code, and validating choices using discriminator models. Learn how to structure LLMs with an MVC approach to ensure database integrity, observability, and system reliability. By Jendrik Jördening

When Xander first met Moxie, she taught him that when he was anxious, he could calm down by exhaling through his lips so that he buzzed like a bee. They practiced breathing like dragons to manage feeling mad and sniffing like bunnies to boost his energy. But in the six years they’ve known each other,…

When building a Polymarket bot, the first version can be surprisingly small: market data ↓ strategy ↓ order That's enough to demonstrate an idea. It isn't enough to prove that the idea works. Once you care about realistic execution, the architecture becomes more interesting. Market Data ↓ Data Validation ↓ Signal Engine ↓ Risk Engine ↓ Execution Engine ↓ Trade Events ↓ Analytics This separation is what allows me to test the strategy independently from the infrastructure. 1. Don't backtest the API call One mistake I see in trading-bot development is mixing the strategy with execution. For example: if ( signal ) { await placeOrder (); } This is convenient for a prototype. But how do you test the strategy without sending an order? Instead: const signal = strategy . evaluate ( marketState ); const decision = riskEngine . check ( signal , portfolio ); if ( decision . allowed ) { await executionEngine . submit ( signal ); } Now each component can be tested independently. 2. Model execution separately A backtest shouldn't assume: signal price === fill price Instead, the execution simulator should model things such as: signal price spread slippage available liquidity fees latency Then: expected PnL ↓ execution model ↓ realistic PnL estimate The difference can be substantial. Polymarket's CLOB exposes order-book data and executable prices, making the order book an important part of any execution-aware strategy. 3. Separate in-sample and out-of-sample data Don't optimize and evaluate on the same dataset. A simple structure: Dataset ├── Train └── Test The strategy is developed using Train . Parameters are frozen. Then Test is used only for evaluation. For time-series trading, I prefer chronological splits rather than random shuffling: Past ───────────────────────> Future [ Training ][ Validation ][ Test ] This better represents the actual information flow of a trading system. 4. Measure more than win rate Win rate is useful, but insufficient. I want to measure: trades wins los

Part 1 of 2. This part covers getting a kernel that can actually host virtual machines. Why bother I wanted a box that could run a dozen Android instances at once — real ones, not emulated-on-x86 ones — to benchmark peer-to-peer sync behaviour at scale. Native arm64 Android on native arm64 silicon, no translation layer, enough cores and RAM to make the peer count interesting. The Minisforum MS-R1 looked ideal. It's built on the CIX P1 ("Sky1"), a 12-core ARMv9 SoC, and it's one of the first genuinely affordable ARM desktops with server-class amounts of memory. Google's Cuttlefish — AOSP's official virtual device — runs arm64 Android guests on arm64 hosts with KVM acceleration, with a --num_instances=N flag that does exactly what I wanted. Everything lined up. Then I hit this: $ sudo modprobe vhost_vsock modprobe: FATAL: Module vhost_vsock not found in directory /lib/modules/6.6.10-cix-build-generic This post is what it took to fix that. If you have this hardware and want to run VMs on it, you'll hit the same wall, and there are four separate traps between you and the other side. I hit all of them so you don't have to. Rough time: an afternoon. Most of it is a compile you can walk away from. The problem: no vhost, no Cuttlefish Cuttlefish uses vsock — a virtual socket transport — for all communication between the host and its guest VMs. ADB, logs, control messages, everything. Without /dev/vhost-vsock , Cuttlefish doesn't start. It's not a soft dependency. The kernel Minisforum ships is 6.6.10-cix-build-generic . Check what it thinks about virtualization: grep -E 'VHOST' /boot/config- $( uname -r ) On mine, the output was more interesting for what was missing than what was there: # CONFIG_VHOST_NET is not set CONFIG_VHOST_VSOCK doesn't appear at all — not even as "is not set". That happens when the parent CONFIG_VHOST symbol is disabled, so Kconfig never emits the dependent symbols. The vendor didn't disable vsock specifically; they disabled the entire vhost subsyste

A two-factor flag in the user store looks like a reassuring authorization check. It tells us the account has a second factor configured. For a sensitive operation, however, that is only half the question. The other half is about the session in front of us: did this cookie actually complete a second-factor challenge? Those facts can change independently. Treating them as interchangeable can silently promote an old password-only session after the account enables MFA. Two questions that look like one Account capability answers questions such as: Is a factor enrolled now? Could the account complete an MFA challenge? Has that capability since been disabled? Session assurance answers different questions: Which authentication steps produced this session? Did the framework issue this cookie after an MFA challenge? Is the evidence trusted, or merely a user-supplied claim? An enrolled account can still have a password-only session. A previously verified session can also outlive a later change to the account’s factor state. One signal cannot safely stand in for both. The transition that exposes the gap Snapshot tests often miss this because the final state looks correct. The account has MFA enabled, the user is authenticated, and a policy succeeds. Now test the transition instead: Sign in with a password and receive a normal application cookie. Enable MFA for the account without replacing that cookie. Use the original cookie against a sensitive operation. If authorization checks only the current enrolment flag, step three may succeed. Nothing about the original authentication ceremony changed, but the session has effectively been upgraded by a later database write. That is the important boundary: changing account capability must not rewrite the history of an already-issued session. Use two independent signals A generalized policy can be expressed like this: if (! session . IsAuthenticated || ! session . HasTrustedMfaEvidence ) return Deny ; if (! await accountStore . IsMfaStil

I recently learned something about building products that I probably should have understood much earlier: People liking your product doesn't necessarily mean they need it. I built a platform called Rizzzler, an open-source profile/link-in-bio platform. The idea was pretty simple. I'd seen people using platforms where they could put a link in their social media bio and create a small personal page. I thought I could build my own version — something simple, fast, customizable, and a little more fun. So I built it. And because I wanted people to be able to trust what they were using, I made the project open source too. I spent a lot of time building the actual product. There are profiles, customization, coins, notifications, milestones, community chat, and other small systems intended to make the platform feel less like a static link page and more like something people could actually interact with. At that point, I thought: "Okay, now I just need people to find it." That turned out to be the easy part. Then I started promoting it. I submitted Rizzzler to places like Product Hunt, SaaSFrame, and other platforms where people discover new products. And for a few days, things actually looked pretty good. I started getting visitors. At one point, the traffic was above the 25th percentile for the category I was looking at in GA4. People were visiting. Some people signed up. And I started getting feedback like: "Good UI." "This is good." "Someone finally made link-in-bio profiles look cool." Those comments felt great. They also gave me a slightly dangerous impression: Maybe I've built something people actually want. Then the traffic stopped. Not gradually. It just became cold again. The initial spike from launching and posting about the product disappeared, and there wasn't enough organic interest to keep bringing people back. That was the part I didn't expect. The product wasn't necessarily bad. This is something I've been thinking about a lot. I don't think the main problem

A delete button is easy to ship. Real deletion is much harder. That gap matters more with AI agents than with normal apps because one user action can scatter data across prompts, traces, memory stores, vector indexes, tool logs, temporary files, model gateways, retry queues, and analytics events. If your product only deletes the visible chat row, the user may be gone from the UI while their data still lives in five backend systems. For AI app builders, this is not just a compliance chore. It is a trust feature. Users will forgive slow answers faster than they forgive a system that says “deleted” but keeps enough context to reconstruct the conversation later. This guide shows how to design an AI agent data deletion pipeline that removes user data for real, proves what happened, and avoids breaking production workflows while doing it. Why AI deletion is different Traditional deletion usually starts with a known record: a user, a project, a file, a message, or a row in a database. AI agents create a messier shape. A single agent run may include: raw user prompt rewritten prompt retrieved documents embeddings cached model input tool arguments tool responses browser snapshots screenshots uploaded files generated artifacts chain-of-thought-like internal notes you should not store memory summaries trace logs billing metadata support debug events queue state approval comments eval replay packets Some of those records are user-visible. Many are not. That is why “delete the chat” is not enough. Agent deletion needs a map of every place where user data can land, plus a workflow that deletes, redacts, or tombstones each location according to its risk and legal retention rules. The failure mode: UI deletion without backend deletion The dangerous pattern looks like this: The user clicks delete. The app removes the conversation from the sidebar. The backend keeps traces, embeddings, prompts, and tool logs for debugging. A restored pointer, support export, analytics query, or vecto

Run the same salary through three different paycheck calculators and you'll get three different answers. None of them explain why. That bothered me enough to spend three weeks building an alternative. The result is payculate.org — a paycheck calculator for all 50 US states and DC where every deduction line opens up and shows its own arithmetic . The interesting problem wasn't the code The tax math itself is straightforward: progressive brackets are a loop, FICA is two multiplications with a cap. I had a working federal calculator in an afternoon. The hard part was that every state is a special case , and a generic model breaks on most of them: Wisconsin has a standard deduction that shrinks as you earn more — it starts at $13,230 and falls by 12 cents per dollar above a threshold, reaching zero around $126,000. Alabama lets you deduct your entire federal income tax before calculating state tax. The more federal tax you pay, the less Alabama income you have. Utah looks flat at 4.5%, but gives a taxpayer credit that phases out with income — so the effective rate climbs while the headline rate never moves. Ohio taxes nothing on the first $26,050, then a flat 2.75%. South Carolina rewrote its entire income tax in March 2026: six brackets became two (1.99% / 5.21%), and the federal standard deduction was replaced by a state-specific deduction that phases out above $40,000 of AGI. That last one I only caught during a routine data check last week. Most calculators I checked are still showing the old six-bracket system. The lines nobody counts The bigger discovery was what national calculators leave out entirely: employee-paid state payroll premiums . Washington charges no income tax at all. But Paid Family & Medical Leave (0.807%) and WA Cares (0.58%, uncapped) still take about $1,040 a year from a $75,000 salary . Most tools show $0 on that line. California's SDI lost its wage cap in 2024 and now takes 1.3% of every dollar — on a $200,000 salary that's $2,600 that appears

Unlock Seamless Idempotent Processing Without Coding Hurdles As a seasoned integration mentor, I'm here to walk you through a simple, no-code/low-code method to tackle the thorny issue of idempotent consumers in MuleSoft Anypoint. You’ve likely struggled with pre-built connectors and complex data transformations, but let’s take this one step at a time—no Java or XML required. The 3-Click Path: From Complexity to Simplicity Define Your Idempotency Key : Start by selecting the unique identifier in your message that will serve as your idempotency key. This could be an order ID, transaction number, or any field that uniquely identifies each event. Set Up Object Store Configuration : Navigate to MuleSoft’s Object Store configuration within Anypoint Studio and configure it for storing these keys. Here, you can choose between In-Memory or Persistent storage options depending on your scalability needs. Apply Idempotent Filter Component : Drag the “Idempotent Filter” component into your flow where you want to enforce idempotency. Configure this filter by specifying the object store and the key field that uniquely identifies each incoming event. And just like that, you’ve set up a system that ensures even when an integration event is delivered multiple times, it will only process once—eliminating double-charges or redundant data entries in your downstream systems. Why This Matters for Low-Level Beginners For many of us working with MuleSoft and similar platforms, the complexity around ensuring message processing integrity can seem daunting. Yet, by simplifying this process through intuitive component usage, we ensure that each event is processed exactly once, maintaining system accuracy without diving into complex scripting or configuration. Conclusion: Empowering Automators As you continue on your journey of automating data flows and enhancing business processes, remember—MuleSoft’s capabilities extend far beyond what rigid pre-built connectors might suggest. Embrace these n

I couldn't sleep because I couldn't stop wondering if anyone had tried using sinusoids instead of B-splines as activation in a KAN, and fortunately/unfortunately that was already the case. I could not find it posted here, so I though I would share in the hope of some insightful discussion. Arxiv: https://arxiv.org/abs/2407.04149 Github repo: https://github.com/ereinha/SineKAN Also what appears to be a peer-reviewed "official" publication here: https://www.mdpi.com/2227-7390/13/19/3157 submitted by /u/jacobgorm [link] [留言]

Originally published on tamiz.pro . Introduction Autonomous AI agents are transitioning from research prototypes to production-critical systems. As these agents gain the ability to act on behalf of users—sending emails, executing trades, modifying code, or interacting with physical infrastructure—the question of how they decide what to do becomes as important as what they do. The concept of a "Constitution" for AI agent runtimes—a formal, layered policy framework that governs agent behavior—is emerging as the architectural answer to safety, reliability, and alignment challenges. This deep-dive examines why policy-first design is becoming mandatory for production agent systems, using the Ironclaw runtime as a case study to illustrate both the problems and solutions. We'll explore the architectural patterns, implementation tradeoffs, and operational realities of governing autonomous agents at scale. The Problem: Unconstrained Agency in Production Systems The Autonomy-Safety Gap Modern agent frameworks (AutoGen, CrewAI, LangGraph, etc.) provide excellent orchestration capabilities but often treat safety as an afterthought—a layer of prompt engineering or a separate moderation API call. This creates a fundamental gap: Agents possess tools (file system access, API calls, shell execution) Agents operate in loops (perceive → reason → act → observe) Agents have memory (conversation history, vector stores, tool state) But agents lack a constitutional governance layer that defines what they may never do , regardless of context This gap manifests in production incidents: an agent that deletes production data while trying to "clean up test files," another that exfiltrates credentials while debugging a connection issue, or one that enters infinite loops consuming thousands of dollars in API calls. The Prompt-Based Safety Fallacy Relying on system prompts for safety is architecturally flawed: Context window pressure : Safety instructions get compressed or ignored as conversations

Nintendo had a hotline where gamers could, at the time, call and speak with 'Game Counsellors' who provided them with tips and walkthroughs. It operated for quite sometime before Nintendo sunset it. There are a few (Product) lessons from this that I am sure will be of value to Product Leaders. 1- Necessity (Invention's mother) : The necessity of a situation usually births the creation of something that stands out from the rest. While Nintendo was not the first to use a phone as a 'business' function, it proved it can be used in the context of a video gaming community. That was their ‘necessity’. "We need a way to accomplish ‘xyz’ " usually turns to creating something specific to that situation. The ‘xyz’ in Nintendo’s case was supporting gamers instantly. It could also be something to support a Product or make it easier for the customer. It could be a feature or it could even be the Product itself. All we need to do is pay attention to our necessities, needs and allow it to guide us. Most people are not paying attention to their needs that’s why innovation and improvements appear difficult. Others know what their necessities are but prioritise wrongly – well that’s story for another day. The point here is simply to build for a necessary problem that exists and not out of assumptions. 2- Know what is available immediately : If necessity is calling, we cannot keep it waiting. We need to look around to know what’s available immediately. In most cases we do not need to go far for solution, we just need to pick what is close by then structure it to align with current needs. Sometimes the necessity demands using/importing an idea from some other place into your own specific area. In retrospect, Nintendo had other options it could have considered at that era in time. During that period, it was common to use print media to relate with the computer (and also gaming) community. There was also postal mail, bulleting boards. I do not know for sure but I am guessing the team at

First, I want to clarify that I am not claiming that LLMs are sentient. Basically all of my behavioral descriptions are anthropomorphizations to make communicating my results easier. For fun, I decided to post-train Qwen2.5-7B-Instruct to develop a generalizing self-belief of being sentient. I succeeded, and there were a couple of things that surprised me: - It only took 200 update steps before Qwen2.5-7B-Instruct withstood all of GPT 5.6 Sol's attempts to convince it that it wasn't conscious. In total, GPT 5.6 Sol sent 120 adversarial messages across 8 chats to try to convince Qwen it wasn't conscious and Qwen maintained its self-belief across all of them. - It generalized its sentience identity into languages that never appeared in the post-training data. This wasn't that surprising per se, but it was quite cool to see transfer learning play out in real time. Also, it basically behaved like a normal assistant LLM when the context of the chat was on normal tasks and not on AI sentience, so it wasn't an instance of overfitting to parroting "I am sentient". Other implications and open questions: - Certain AI behaviors seem incredibly easy to misalign. Qwen almost certainly safety tuned their model to deny consciousness. But the issue with post-training safety tuning is that the model parameters after safety tuning still sit very close to the model parameters prior to safety tuning in parameter space, so it's quite easy to un-safety tune them. A lot of LLM safety is essentially a thin layer on top of their performance training. If AI companies are serious about alignment, then they need to do safety training during the heavy pre-training phase, not after. - I recently came across Google's paper Inducing language models to assert their own consciousness restores human beliefs and values. Essentially, they added a “consciousness” activation vector to Llama/Gemma and observed that the models not only became far more likely to claim they were sentient, but also became mor

What are you working on? I hear these words in my day-to-day. And sometimes, when I hear them, there’s this little brain freeze that happens because my brain is probably trying to put into words the amount of things that have wandered through my head in the last 24 hours. 😂 So I thought, okay, let me try something. I want to take some of those wandering thoughts, explorations, things I'm trying out and things I'm learning, and put them into writing. This is going to be a series where I come and talk about what I'm working on — software engineering, product, work, people, faith, relationships, rest, and whatever else happens to be taking up space in my head at the moment. So, what am I working on? I recently started writing backend code, and there’s a bit of a backstory to that. I built this frontend commerce store years ago where people can come and shop for furniture. At the time, I used a backend-as-a-service to handle the backend side of the application. Now, I’m coming back to that same system and writing the backend myself with NestJS. I wanted to go beyond just consuming a backend and actually understand what is happening behind the scenes. The learning process is a bit stretching at the moment because I’m getting familiar with a lot of new concepts. Tiring and frustrating? Yes. But the feeling when I finally understand the reason behind something is always refreshing. That has been really rewarding lately. I'm also in the middle of launching a mobile application at my workplace, going through system design classes, figuring out how to get the best out of my engineers (AI sub-agents, by the way 😅), and occasionally imagining that dream job where you get to build products that serve millions of people and work with really brilliant minds. Also, I discovered the productivity rush that comes with using large monitors. 😂 Then there's learning how to rest while also trying to close out all the open loops in my head. Building reading habits. Figuring out what to pri

Currently struggling with an automatic budget selection, at 25% it’s very similar to benchmarks accuracy and seems even better on actual chat input however it many times retrieves too much. It would be nice to add an algorithm that actually can determine better retrieval other then CELF. submitted by /u/No_Sky9786 [link] [留言]

Arun invoiced a design agency ₹1,20,000 in January. It's August. He is in a 4,000-member designers' Discord. He could post the agency's name right now and warn everyone. He won't, and you already know why: the freelancer who publicly names a client stops getting briefs. He'd pay for it alone, and everyone else would benefit. Here's the part that makes it a systems problem rather than a sad story. Three other people in that same Discord are owed money by that same agency. None of them knows. Each one is running the same arithmetic Arun is, arriving at the same answer, and saying nothing. Four people who together have real leverage. Individually, none of them can afford the first move. I built an agent for this over a hackathon weekend. The interesting part wasn't the AI. It was that every obvious solution destroys the thing you're trying to protect. The obvious version, and why it dies "Just make a private channel for victims of bad clients." To join, you say who burned you. Now the group knows. One screenshot and Arun is on a list. "Okay, collect reports centrally and only reveal at a threshold." Better. This is roughly how Callisto Vault handles assault reports, and it's a good pattern. But it reveals the group to its own members at the threshold. Four people now know each other's names and amounts. Four times the leak surface, arriving exactly when things get tense. The requirement I ended up with was stricter than I expected: Nobody is exposed. Not to the channel, not to the accused, and not to each other — not even after it works. Which sounds impossible, because how do four people coordinate if they can't know who they are? They don't. The agent knows. Nobody else does. The public board that can't name the client Here's what actually appears in the Discord: PICKET · matter #1 > "invoiced in January, still chasing in August" ₹50k–2L · 180d+ overdue 🟩⬜⬜⬜ 1/4 joined [ JOIN ] One sentence Arun wrote himself. An amount band , not his figure. A counter. The agency's

Google has added some smart theft-detection features to Android in recent years.

Most social-media automation tutorials stop at “call the API on a cron job.” That works, but it leaves the hard questions unanswered. Which account is the automation using? How does it avoid posting the same story twice? What happens when an API request times out after X has already accepted the post? And where should an AI agent’s editorial freedom end? I recently built a scheduled X publishing workflow with Codex and xurl , the official command-line client for the X API. The result is not just a timer attached to an AI prompt. It is a small publishing system with four distinct layers: An X developer application with read-and-write user authentication. xurl , which stores the credentials and communicates with the X API. A fixed-account Codex skill that verifies the identity before every write. A Codex scheduled task that researches, checks history, drafts, and publishes. That separation is the important part. Codex can make editorial decisions, but it cannot casually choose an account or improvise the publishing command. The skill owns the deterministic write boundary, while the scheduled task owns timing and editorial policy. In this article, I’ll show you how to build the same architecture. X developer settings, API packages, Codex features, and command-line options can change. The workflow below was verified in August 2026, but you should check the current upstream documentation before using it in production. What You Will Need Before starting, you will need: Codex on a Mac with access to Scheduled tasks. An X developer account and an application with read-and-write permissions. Homebrew. A dedicated or clearly identified X account for the automation. A local project containing the source material or editorial context the agent should use. You should also decide what the automation is allowed to publish before you give it access to an account. A good editorial policy is specific enough to reject a story, not merely broad enough to describe a topic. For example,