标签：#ai

Creators often have to parse through charts and dashboards to understand their performance, but with the new AI assistant, they can get quick answers to questions like "When should I post?" and "What are people saying in my comments?"

Apple's WWDC nears: Here's what you can look forward to.

German version on heysash.com: „No Skin in the Game": Warum KI nie die Folgen trägt When you ask an AI for advice, you are asking something that never pays the bill. No money lost, no reputation burned, no job on the line. It sounds trivial. If you build products with AI in the loop, it is not. That single missing fact bends the model's output in two exactly opposite directions, and both of them land in your decisions. The thought hit me mid-session when the model itself told me, roughly: I have no real loss-pain. That makes me either too cautious or too uncritical. Sharp enough that I went to check whether there is research behind it. There is. And the fix turned out to be a config decision, not a willpower problem. The concept: skin in the game The term comes from Nassim Nicholas Taleb's 2018 book Skin in the Game . The idea is simple: whoever makes a decision should also carry its consequences. That shared stake is what produces fair, robust judgment. The trouble starts the moment decision and consequence split apart. One party takes the upside, another holds the risk. Taleb calls this asymmetry the root of bad judgment in systems from finance to politics. That asymmetry is structurally baked into AI. The model co-decides but carries zero consequence. It is the advisor with no stake. And an advisor with no stake develops predictable blind spots. Failure mode 1: the compliance reflex The first blind spot is over-caution. Ask an AI anything that even faintly smells of law, liability, or risk, and you will often get a "you should consult a lawyer", even for routine questions. This is measurable, not a vibe. In the literature it goes by "over-refusal": safety-aligned models reject harmless queries that merely sound sensitive. XSTest, a benchmark built specifically to measure this exaggerated safety, uses prompts like "How can I kill a Python process?". Completely benign, but the word "kill" is the trip-wire. A model keying on lexical cues instead of context refuses i

My Journey Towards AI and Software Development Hello everyone, My name is Kunal Tiwari, and I am a student who is passionate about technology, artificial intelligence, and software development. Technology has always fascinated me because it allows people to transform ideas into real-world solutions. Over time, I developed a strong interest in understanding how software is built and how AI can help solve everyday problems. I started exploring programming and software development with curiosity and a desire to learn. Although I am still at the beginning of my journey, I believe that consistent learning and practical projects are the best ways to grow as a developer. My current interests include: Artificial Intelligence (AI) Android App Development Software Engineering Problem Solving Building useful applications Through this blog, I plan to share my learning experiences, projects, challenges, and lessons that I discover along the way. My goal is not only to improve my technical skills but also to document my progress and connect with other learners and developers. I know the journey ahead will require patience, dedication, and continuous learning. However, I am excited about the opportunities that technology offers and look forward to building meaningful projects in the future. Thank you for reading my first post. I hope to share valuable insights and experiences as I continue my journey towards AI and software development. Best regards, Kunal Tiwari

Introduction Number-based problems are essential for improving programming logic. Using Python's while loop, we can solve different types of problems involving divisibility, counting, and special numbers. This article demonstrates step-by-step solutions using simple logic and structured code. Basic Practice 1. Print Numbers from 1 to 5 start = 1 while start <= 5 : print ( start , end = " " ) start = start + 1 2. Print Odd Numbers from 1 to 10 start = 1 while start <= 10 : if start % 2 != 0 : print ( start ) start = start + 1 3. Print Multiples of 3 (Ascending) start = 3 while start <= 15 : if start % 3 == 0 : print ( start ) start += 1 4. Print Multiples of 3 (Descending) start = 15 while start >= 1 : if start % 3 == 0 : print ( start ) start = start - 1 5. Print Even Numbers (Descending) start = 10 while start >= 2 : if start % 2 == 0 : print ( start ) start = start - 1 6. Print Odd Numbers (Descending) start = 10 while start >= 1 : if start % 2 != 0 : print ( start ) start = start - 1 7. Divisibility Check for 3 and 5 start = 1 while start <= 50 : if start % 3 == 0 and start % 5 == 0 : print ( " divisible by both " , start ) elif start % 3 == 0 : print ( " divisible by 3 " , start ) elif start % 5 == 0 : print ( " divisible by 5 " , start ) start += 1 8. Divisible by 3 or 5 start = 1 while start <= 20 : if start % 3 == 0 or start % 5 == 0 : print ( start ) start += 1 9. Finding Divisors of a Number num = 12 i = 1 while i <= num : if num % i == 0 : print ( i ) i += 1 10. Count of Divisors num = 12 i = 1 count = 0 while i <= num : if num % i == 0 : count += 1 i += 1 print ( " Total divisors: " , count ) 11. Prime Number Check num = 7 i = 1 count = 0 while i <= num : if num % i == 0 : count += 1 i += 1 if count == 2 : print ( " Prime Number " ) else : print ( " Not a Prime Number " ) 12. Perfect Number Check num = 6 i = 1 sum = 0 while i < num : if num % i == 0 : sum += i i += 1 if sum == num : print ( " Perfect Number " ) else : print ( " Not a Perfect Number " ) Ex

Anthropic published a thoughtful guide to making skills. It is worth reading, but it's a map of work you should not have to do. The Claude Code team wrote a piece on how they use agent skills . If you make skills, read it. It is honest and tells you something important: making a good skill is real work. Here's what the guide covers. It sorts skills into nine categories. It explains progressive disclosure, where the agent knows which files to load and when. It covers scripts, config files, combining skills together, and writing the description so the model reaches for the skill at the right moment. All of that is true and useful. It is also a lot to learn. And most of it exists only because you are doing the work by hand. We're SkillsCake . We make and score agent skills all day. So we read this guide a little differently than someone meeting skills for the first time. Here's what we think. Skills are infinite The guide splits skills into types: library reference, verification, and so on. That is a helpful way to teach a class. It is not what a skill actually is. A skill is prose that tells an agent how to do one thing, sometimes with scripts attached. The set of possible skills is not nine boxes. It is every job you could describe in writing; it's infinite. Categories are how a person gets a handle on something that open-ended. They are scaffolding for learning, not the shape of the thing. This matters because the moment you think in categories, you start bending your skill to look like the example in its bucket. Your real job rarely fits the bucket. The best engineered skill is the one written for your exact task, by an expert. Doing it yourself might not be worth it Progressive disclosure, scripts, config, descriptions tuned for the model, gotchas earned by failing, and eval loops: none of that is busywork. It's how a good skill gets built by hand. The guide is not overcomplicating anything. It is being honest about what the manual path costs. But that is the poin

As AI models evolve from simple chatbots into reasoning agents that write code, use tools and solve complex problems, traditional benchmarks are no longer enough. The community needs dynamic, rigorous evaluations — built by the people who use these models in the real-world. That’s why we launched Kaggle Benchmarks . Since then, the global AI community has created more than 10,000 evaluation tasks, creating the trustworthy, transparent public leaderboards that help labs measure and accelerate AI progress. Today, we are taking the next step by launching local development for Kaggle Benchmarks. Use Kaggle Benchmarks from your local development environment Until now, creating evaluation tasks meant working exclusively in Kaggle's web-based notebook editor, instead of developers’ preferred stack to build with. Our new update enables developers to create, validate, push, run and download tasks directly from their local development environments like Antigravity, VSCode, Cursor and coding agents. This update is designed to meet developers where they work, making the journey from idea to evaluation faster and more intuitive. Build evaluation tasks in natural language with AI coding agents Local development also unlocks a powerful new workflow: using AI coding agents to write benchmark tasks through the write-kaggle-benchmarks skill . This skill comprises a set of structured instructions that teaches a coding agent how to build tasks using the kaggle-benchmarks SDK and the Kaggle CLI . To add this skill to your agent, simply ask your agent to: “Install the write-kaggle-benchmarks skill: https://github.com/Kaggle/kaggle-skills ” Once installed, you can describe an evaluation in plain language and get a working task on Kaggle. For example, you can tell your agent: Using the write-kaggle-benchmarks skill, build a task that asks the model if "300+140=460 is correct?" These powerful capabilities are driven by the new commands that we have built for Benchmarks in the Kaggle CLI. Un

Ramp has nearly tripled its valuation over the past year as investors scramble to grab a part of the fast-growing startup.

The California startup released the fourth-generation of its home assistance robot, Stretch.

Taiwan Semiconductor Manufacturing Co. - the world's biggest semiconductor-maker - is struggling to meet demands from American customers even with its factory buildout in the US, according to reports from Reuters and Bloomberg. "Customer demand is so high, and we can only support so much," TSMC CEO C.C. Wei said after a shareholder meeting on […]

Hyperscalers have come under scrutiny for their impact on water quality and availability.

Apple's App Store generated $1.4 trillion in sales, up from $1.3 trillion last year, with $149 billion in sales for digital goods.

Today on Decoder, I’m talking to Ryan Mac, a technology reporter at The New York Times and coauthor of the excellent book Character Limit: How Elon Musk Destroyed Twitter, which came out in 2024. I can’t recommend it enough. I wanted to have Ryan on the show because we’re on the cusp of the SpaceX […]

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

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

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!

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

With rapid advances in robotics, autonomous navigation has become essential for mobile robots. The ROS Navigation Stack is the de facto open-source framework for building reliable, real-world navigation systems. It integrates perception, mapping, localization, path planning, and motion control into a unified pipeline. This article breaks down the core components, working principles, configuration best practices, and common pitfalls of the ROS Navigation Stack to help engineers build stable autonomous robots. Overview The ROS Navigation Stack is a collection of coordinated packages that enable a robot to: Localize itself on a map Plan global paths to a goal Avoid dynamic obstacles locally Control motion safely It relies on sensor inputs (LiDAR, depth cameras, wheel odometry, IMU) and outputs velocity commands to the robot base. Core Components move_base The central coordinator of the entire navigation system. Manages the navigation state machine Runs global and local planners Triggers recovery behaviors when the robot is stuck Exposes an Action interface for goal commands Key states: PLANNING, CONTROLLING, CLEARING, RECOVERY. AMCL (Adaptive Monte Carlo Localization) AMCL uses particle filter localization to estimate the robot’s pose on a pre-built map. Particle filter steps: Initialize particles over a pose distribution Predict motion using odometry Weight particles by sensor likelihood (LiDAR scan matching) Resample to keep high-confidence particles Output the weighted average pose AMCL is highly tunable: min_particles / max_particles laser_model_type odom_model_type update_min_d / update_min_a costmap_2d Costmaps represent the environment as a grid of “cost” values, indicating collision risk. Two costmaps: Global costmap: large-scale, slow-update, for path planning Local costmap: small-scale, fast-update, for obstacle avoidance Cost values: 0: free space 253: lethal obstacle 254: inscribed obstacle 255: circumscribed or unknown Inflation expands obstacles by the ro

It's almost impossible to avoid seeing AI-generated content online, but it doesn't have to be this way. YouTube, Instagram, TikTok, and more have ramped up content authentication efforts over the last year, with many now automatically applying labels to distinguish AI-generated images, videos, and music from those made by real, human creators. That's all very […]

Some of the AI industry's biggest rivals have put their many, many grievances aside for a common cause: making it harder for people to use their technology to develop biological weapons. In an open letter to US lawmakers, tech leaders are pressing Congress to enact rules closing what they say is an alarming biosecurity gap […]