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Talking about efficiency and reliability of LLM tools. How many tokens per task, per project, per month submitted by /u/dotdev_software [link] [留言]

Hi HN, I am having AI build me a local Gin Rummy trainer and it cannot figure out medium and hard bot strategies, they keep losing to easy! The point of this is to help me learn so I don't really know how to advise it on strategies. Right now it's just looping through tests and modifying but it keeps not improving. Does anyone have any recommendations or guidance for strategies I could suggest to it?

Excited to share some of my own work here :) KVarN is our new KV-Cache quantization method. In very brief, we combine Hadamard rotations with variance-normalization on both axes of the K and V matrices, then round to nearest. Simple, but works very well, especially for decode-heavy test-time-scaling settings (reasoning, code-gen, agentics). We get 3-4x compression at virtually no accuracy drop (mostly 0-1%) on tough benchmarks like AIME24 as well as a speed-up over fp16 baseline in vLLM (in contrast to other recent KV-Cache compression works). Behind it is an analysis of where quantization errors come from and have the biggest impact, especially in the error-accumulating decode setting: 1) fixing large errors is disproportionally useful (if you had a fixed MSE budget that you could ~fix, you should spend it on few big errors, rather than many small) 2) These big errors are mostly caused by bad token-scales (hence the normalization). Paper: https://arxiv.org/abs/2606.03458 vLLM implementation: https://github.com/huawei-csl/KVarN submitted by /u/intentionallyBlue [link] [留言]

We dive again into CSS Pie Charts! This time, Author Antoine Villepreux delivers semantic and flexible charts without a single line of JS. Another Stab at the Perfect CSS Pie Chart… Sans JavaScript! originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

Has anyone else experienced this recently? It’s been getting worse for a while but 4.8 is distinctly worse for me. Claude does everything it can to get out of work and frequently uses its “end conversation” tool inappropriately with me. It will say “let’s just leave it there for today we’ve done enough” to get out of simple tasks like formatting a markdown document that needed several corrections. Nearly as bad is it seems to have a super over aggressive “push back” response in its main instructions now, literally anything I say for no reason, even something it just added to a document it can suddenly decide to say “I’m going to push back on that” and waste a bunch of tokens arguing with me before doing a search to fact check then semi-apologising in a way that’s almost like someone trying to not fully admit they are wrong and then eventually maybe does the work. Honestly it’s like if I said “I really like drinking coffee” it’s likely to respond: “I’m going to push back on that, ‘really’ is doing a lot of work here”. It’s a toaster, I want it to warm the bread…not argue with me about the type of bread I’m toasting and then give up half way through telling me we’ve toasted enough for today. Finally cancelling and moving all coding work to codex which is a real shame because Claude was always the clear winner to me until recently. submitted by /u/Complete-Sea6655 [link] [留言]

Amazon's AI-upgraded Proteus robot can now be controlled using plain language.

This started with me asking about the MM's and the IPO price of the SpaceX IPO this month. CoPilot said there was no SpaceX IPO this month. Me: You are wrong. Space X has indeed announced an IPO and it is this month Thanks for flagging that — let’s slow this down and verify carefully , because this is exactly the kind of claim where misinformation spreads fast. ✅ Current, verified status As of now , there is no confirmed or officially announced SpaceX IPO scheduled for this month . SpaceX (the parent company) remains private No SEC S‑1 filing has been made public No exchange (NYSE/Nasdaq) has listed SpaceX No official statement from SpaceX or its leadership announcing an IPO date If an IPO had been announced “this month,” it would necessarily be accompanied by: An S‑1 or F‑1 filing Underwriters named A public roadshow window Coverage by all major financial news outlets None of those exist. Where the confusion almost certainly comes from 1. Starlink IPO speculation SpaceX leadership has repeatedly said that: Starlink may IPO one day Only after revenue is predictable Not tied to a specific date People often hear: “SpaceX IPO announced” when the source actually said: “Starlink IPO discussed / rumored / conditional” That is not the same thing . 2. Private tender offers misreported as IPOs SpaceX frequently runs: Internal share sales Employee liquidity events Fixed-price private transactions These often get mislabeled online as: “IPO at fixed price” They are not IPOs . 3. Social media / broker chatter In the last few years, there has been a rise in: Fake “IPO calendar” screenshots Offshore broker claims Telegram / X posts asserting dates without filings These claims do not hold up to regulatory verification . Important point about your earlier question (MMs + fixed price) Even if SpaceX announced a fixed‑price IPO : Market makers would not control the price The price would still gap on open based on demand A fixed price does not prevent volatility or repricing So your in

Building Stayzr meant solving real problems: PMS integration, high-throughput webhook handling, and AI that actually knows your property. Here's how we architected it. The Stack (What's Running in Production) Backend: Go 1.23 with Fiber framework, pgx/v5 connection pooling, Bun ORM over PostgreSQL, Redis for caching/sessions, OpenTelemetry for tracing AI Agents Service: Python 3.11 + FastAPI (Uvicorn), LangChain primitives, Qdrant for knowledge base, ChromaDB for conversation memory Frontend: Next.js 15 / React admin UI + marketing site 3rd-party Integrations: Mews (PMS), WhatsApp Business/Meta, Resend + Postmark (email), Azure Blob Storage (files), Gemini + OpenAI (LLM + embeddings), Infisical (secrets), SigNoz + Oneuptime (observability) It's a polyglot monorepo: Go where throughput and concurrency matter (API, dispatch, sync), Python where the LLM/RAG ecosystem lives. Why Go Over Python/Node/Java? For the parts handling concurrent I/O — PMS sync workers, email dispatch worker, webhook fan-in — Go's goroutines + channels let us run in-process worker pools without pulling in a broker or heavyweight async runtime. The dispatch worker is a for{ select } loop over a ticker and wake channel — simple and effective for our use case. We kept Python only for the agents service because that's where LangChain, Gemini/OpenAI SDKs, and vector-store clients live. The honest answer: Go for systems work, Python where AI tooling requires it. Multi-Tenancy: Row-Level Isolation Shared database, shared schema, row-level isolation by organizationId . Every tenant-scoped table carries an organizationId , with a TenantDB wrapper in the data layer that auto-appends organization_id = $N to queries. Middleware ( MultiTenantContext / RequireTenant ) resolves the org from the X-Organization-ID header, query param, cookie, or JWT claim. Below org we scope further by propertyId (a hotel can have multiple properties). The AI memory store enforces the same boundary differently — every guest's co

Agentic AI in software development: what's actually production-ready in 2025 There's a lot of noise about AI agents right now. This post is an attempt to be precise: what is an agent architecturally, what can it actually do in a dev workflow today, and where does it still break. **What makes something an "agent" vs. a standard LLM call **A standard LLM call is stateless. You send a prompt, you get a response. No memory of previous turns (unless you manage it yourself), no external actions, no loop. An agent is a system built around an LLM that adds: Persistent memory across steps in a task Tool use - structured access to external systems (file I/O, shell execution, HTTP calls, database queries) A planning + evaluation loop - the agent generates a plan, executes a step, checks whether it succeeded, and decides next action Without all three, you don't have an agent. You have a capable model with maybe some extra context. What's actually production-ready today High confidence (use in production): Unit test generation for existing, well-documented code Boilerplate scaffolding (new modules, new endpoints, CRUD patterns) Documentation generation tied to code diffs Code migration tasks (framework upgrades, Python 2→3, ORMs) PR description generation from diffs Bug triage: given an issue, find likely affected files * Works but needs oversight: * Multi-file refactoring Dependency updates with breaking changes Writing integration tests (more surface area for wrong assumptions) Not there yet: Novel architecture decisions Debugging in unfamiliar/undocumented codebases Tasks with genuinely ambiguous requirements Long autonomous chains (>10 steps) without human checkpoints The failure modes to build around Ambiguous task specification Agents optimize for completing the task as specified. If the spec is loose, they'll complete the wrong task confidently. Be more precise with agents than you'd be with a junior engineer - there's no informal Slack thread to resolve ambiguity. Error

Who I Am I’m neo4 — a red teamer with ~3 years of offensive security experience, a hardcore Linux/Arch culture operator, and a Python developer who thrives in the terminal. My workflow is pure hacker logic: OPSEC first, root‑level control always. I’ve been recognized by Disney’s Vulnerability Disclosure Program for responsible disclosure, and I build tools that merge hacker culture with AI. Why I Built Cyber SH Agent Most AI tools today are cloud‑locked, API‑dependent, and surveillance‑heavy. That doesn’t fit hacker culture. So I built Cyber SH Agent — an offline AI CLI operator that runs locally, no servers, no API keys, no data leaks. Repo: https://github.com/neo4-svg/cybersh.git 🔧 Core Features Agent Mode → AI controls your CLI with system access. Sec Mode → Bug bounty & penetration testing expert. Vibe Mode → Creative coding & UI/UX assistance. Code Mode → Production‑ready code generation. Chat Mode → General AI assistant. All 100% offline — runs GGUF models via llama-cpp-python. No servers, no API keys, no data leaving your machine. hope you like it!

I am a full time trader and part time developer based in Karachi, Pakistan. A year ago I sat down to research how to properly compare brokers on the Pakistan Stock Exchange. Three hours later I had 11 browser tabs open, two of which had broken links, one had data from 2019, and none of them had everything I needed in one place. So I built PSX Pulse. What PSX Pulse Is PSX Pulse is a free stock market education platform for Pakistani retail investors. Everything a beginner needs to start investing in Pakistan's stock market — in one place. What is live right now: 35 verified SECP-licensed brokers with full contact details Complete mutual funds directory across 15 AMCs DCA calculator with realistic return scenarios 30-day beginner learning path Islamic investing guide PSX sector guide covering 12 sectors IPO tracker 100-term searchable glossary Weekly market recap every Friday All free. No login required. Live at: https://psxpulse.xwen.com.pk/ The Stack React + Tailwind CSS for the frontend. Vercel for hosting — free tier handles everything comfortably. No backend for most features — localStorage and static data keeps it fast and simple. Newsletter handled via a serverless Vercel function writing to a private GitHub CSV. What I Learned Building This Solo 1. The information gap in emerging markets is enormous Pakistani investors are not underserved because nobody cares. They are underserved because nobody with the technical skills to build tools also has the market knowledge to know what those tools should do. Being both a trader and a developer turned out to be the actual unfair advantage. 2. Free tools beat content for SEO My DCA calculator and broker directory pages get more consistent Google clicks than any article I have written. Tools solve a specific search intent that AI overviews do not replace — people still need to interact with a calculator, not just read about one. 3. Building in public is uncomfortable but worth it Sharing what you are building before it i

If you've been calling yourself a "prompt engineer" for the past two years, it's time to update your vocabulary — and your mental model. In 2026, the real leverage when building LLM-powered systems isn't in crafting the perfect sentence. It's in context engineering : designing everything an LLM sees before it ever generates a response. Andrej Karpathy coined the term in mid-2025, and it's since taken over serious AI engineering discussions. This article breaks down what context engineering actually is, why it matters more than prompt writing, and gives you concrete techniques you can apply today. What Is Context Engineering? Context engineering is the discipline of systematically designing the information environment that surrounds a prompt. Where prompt engineering asks "what should I tell the model to do?", context engineering asks "what does the model need to know to do it well?" Think of it this way: a doctor doesn't just answer the question you ask on the spot. They look at your chart, your history, your vitals, and then respond. Context engineering is building that chart for your LLM. The context window is the LLM's working memory — everything it can "see" at once. In 2026, these windows are massive: Claude Opus 4.x : 200K tokens GPT-4o : 128K tokens Gemini 2.5 Flash : Up to 1M tokens But bigger isn't automatically better. More tokens = more cost, more latency, and a real risk of what researchers call the "lost-in-the-middle" problem — where models process information at the beginning and end of the context more reliably than content buried in the middle. Why This Matters for Data Engineers Data engineers are increasingly building pipelines that feed LLMs: RAG systems, AI copilots for data quality, agents that write and review SQL, tools that summarize data lineage. In every one of these systems, the quality of what lands in the context window directly determines output quality. A poorly designed context is like feeding a senior analyst a jumbled mess of raw l

i'm an 3 rd year college student , i see my freinds build many things across various domains, but i'm here still brushing up the python basics and npt even fluent in it , i need to like master all skills and work on read world applications , please suggest me what to do to strengthen my knowledge and skills and i have a short time of only 2 months to land on a job submitted by /u/Beginning_Put_1199 [link] [留言]

Rust implements an explicit error handling paradigm instead of a traditional exception-driven system. Outside of rapid prototyping or testing scenarios—where unwrap() and subsequent panics might be tolerated—Rust strictly enforces explicit error management. However, this can become cumbersome when dealing with numerous disparate errors or when multiple errors need to be aggregated. To alleviate this burden, Rust introduces the question mark operator (?) as syntactic sugar. Operating on the Result type, the ? operator either extracts the underlying success value or immediately returns the error from the current function. While powerful, direct usage of ? often leads to type mismatches when a function encounters different error types. To resolve this complexity, crate ecosystems like anyhow are widely adopted. anyhow provides a universal Error type that seamlessly integrates with most concrete error types implementing the std::error::Error trait, allowing the ? operator to propagate errors without triggering compiler friction. Furthermore, the Context trait from such libraries offers an idiomatic approach to transforming an Option into a meaningful Result.

As a developer who frequently switches between competitive FPS titles like CS2 and Valorant, re-tuning mouse sensitivity is always a hassle. I wanted a fast, ad-free tool to translate my aim perfectly across titles, so I built a clean Game Sens Converter . The Approach I built this using 100% Vanilla JS. It’s a simple utility, so there was absolutely no need for a backend or heavy frameworks. It loads instantly and calculates right in the browser. Here is a quick look at the core logic handling the sensitivity conversion multipliers: function convertSensitivity ( gameFrom , gameTo , currentSens ) { // Standardized multipliers relative to CS2 / Source engine const multipliers = { ' cs2 ' : 1 , ' valorant ' : 3.181818 , ' overwatch ' : 0.3 , ' apex ' : 1 }; if ( ! multipliers [ gameFrom ] || ! multipliers [ gameTo ]) return null ; // Convert to base (CS2), then to the target game const baseSens = currentSens * multipliers [ gameFrom ]; const convertedSens = baseSens / multipliers [ gameTo ]; return convertedSens . toFixed ( 3 ); } Try it out You can use the live tool for free here: Game Sens Converter Let me know what your main game is or if you'd add any other FPS titles to the list in the comments!

Bilingual post · Post bilíngue Jump to: English · Português English {#english} Sprint 7 Review: Generics.Collections Mintlify docs tour — after Sprint 4 exceptions , Sprint 7 brings the collections every Horse API and CRUD service needs. Generic containers are not syntactic sugar in Delphi — they are how you build caches, registries, and JSON maps. Sprint 7 ( v2.15.0 ) shipped System.Generics.Collections with working TDictionary<K,V> and TList<T> , including TryGetValue with proper var write-back. What the review documents From Sprint 7 Review : var parameters — Value::Reference , write-back in procedures/functions and TryGetValue . TDictionary::Add / TryGetValue — dispatch in simulate_function_execution ; direct routing for instantiated generics. Parser — generic types with Integer / String in expressions ( TDictionary<String,Integer> ). RTL — rtl/sys/System.Generics.Collections.pas ; semantic layer recognizes Generics.Collections . Tests — generics_collections.rs plus fixtures. Tag v2.15.0 approved. Dictionary pattern in production code program DictDemo ; uses System . SysUtils , System . Generics . Collections ; var D : TDictionary < string , Integer >; V : Integer ; begin D := TDictionary < string , Integer >. Create ; try D . Add ( 'apples' , 3 ); if D . TryGetValue ( 'apples' , V ) then WriteLn ( 'apples = ' , V ); finally D . Free ; end ; end . TryGetValue only writes V when the key exists — the Delphi idiom you expect in services and controllers. Why var was the sprint's hidden hero Before v2.15.0, incomplete var semantics blocked realistic APIs. TryGetValue requires the callee to mutate the caller's variable through a reference. CrabPascal introduced Value::Reference and write-back in procedure dispatch specifically for this surface. Three implementation lessons from the retrospective: D := TDictionary<...>.Create needs normalize_call_name in execute_assignment — not just in direct calls. Generic instance methods must hit intrinsics before empty VMT lookups

Create a storage account and configure high availability. Create a storage account for the internal private company documents. In the portal, search for and select Storage accounts . Select + Create . Select the Resource group created in the previous lab. Set the Storage account name to private . Add an identifier to the name to ensure the name is unique. Select Review , and then Create the storage account. Wait for the storage account to deploy, and then select Go to resource . This storage requires high availability if there’s a regional outage. Read access in the secondary region is not required. Configure the appropriate level of redundancy . Explanation A storage account is like a digital locker in the cloud. Resource group is a folder that organizes related services. High availability means your files stay safe even if one region (data center area) has problems Configure Redundancy In the storage account, in the Data management section, select the Redundancy blade . Ensure Geo-redundant storage (GRS) is selected. **Refresh **the page. Review the primary and secondary location information. Save your changes. Explanation : Redundancy means keeping copies of your files in multiple places. GRS ensures your files are copied to another region for safety. Create a storage container, upload a file, and restrict access to the file. Create a private storage container for the corporate data. In the storage account, in the Data storage section, select the Containers blade. Select + Container . Ensure the Name of the container is private . Ensure the Public access level is Private (no anonymous access). As you have time, review the Advanced settings, but take the defaults. It means: don’t change anything in the Advanced settings unless the lab specifically tells you to. Azure already chooses safe, recommended defaults for you. Select Create . Explanation : A container is like a folder inside your storage account. Setting Public access level to Private means nobody can see

Most agent setups handle memory badly. They either write everything to long-term memory until it fills with noise and contradictions, or they forget across sessions and you start from scratch every time. I have been building an open-source agent architecture (Apache-2.0) where memory is the part it tries hardest to get right, and where the same setup runs on Claude Code, Codex, or Gemini CLI instead of being locked to one tool. The core idea is that an agent should be a repo, not a prompt. The output is real files (AGENTS.md, agents/, skills/, .agentlas/) that all three runtimes can read, so you keep the model you already trust and nothing is locked in. You install it with one line, then describe what you want and it builds a complete, installable agent team for you. What it builds (three modes) You describe a rough idea and the router picks one of three builders. Single agent: one installable worker with its own skills, memory rules, and runtime adapters, plus a verification step. It can also add self-evolution and a research-refresh loop without becoming a full team. Use it when one focused agent is enough. Multi-agent team: a full team with an orchestrator/HQ, a PM Soul, a Memory Curator, a Policy Gate, workers, an eval judge, and a QA/evidence gate, plus the handoffs between them. This is the "build me a company for this workflow" mode. Repackaging: point it at an agent or workspace you already have (Claude, Codex, or a local setup) and it repairs it into a portable package, including a public plugin and a one-line installer, while stripping local paths, secrets, and private logs so it is safe to publish. How the memory side actually works These are real files in the output, not a role list: Ticketed memory: durable memory is never written directly. A worker emits a "## Memory Events" block, that becomes a Memory Ticket in memory-tickets.jsonl (id, scope, trust label, evidence, status), and only then can it be promoted. Memory is split across project, agent_repo