Startups & Ventures

⚙️ Stapply: Your AI job agent A new AI tool called Stapply promises to make job hunting effortless by acting as a personal AI recruiter, finding, ranking, and even applying to roles for you. 🔸 Indexes all existing vacancies for your search across multiple sources in real time. 🔸 Ranks results based on your preferences and career goals. 🔸 Automatically fills out application forms and attaches your resume. 🔸 Sends the applications directly, eliminating tedious manual steps. 🔸 Works as a personal assistant that adapts to your job-hunting style.

By taking over both the search and application process, Stapply could turn the often frustrating task of job hunting into a seamless, AI-powered matchmaking experience.

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🔍 Wikipedia’s knowledge engine, rebuilt for AI Wikimedia Deutschland, Jina.AI, and DataStax unveiled the Wikidata Embedding Project, a revamped interface to make Wikipedia’s 120 million+ entries AI-ready. The goal is to let LLMs query not just keywords, but meaning, relationships, and context. 🔸 Converts Wikidata into vector embeddings, enabling semantic search rather than just keyword or SPARQL queries 🔸 Supports the Model Context Protocol (MCP) so AI systems can plug in Wikipedia as a live knowledge source 🔸 Queries return richer context, e.g. “scientist” yields related fields, translations, images, linked concepts 🔸 Openly hosted (on Toolforge) and free for developers to use 🔸 Designed to help retrieval-augmented generation (RAG) systems ground answers in verified knowledge

With this, Wikipedia moves from passive data pile to active real-time feed for AI. The irony: the world’s largest crowdsourced encyclopedia is becoming one of AI’s most reliable knowledge backbones.

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🛠 Thinking machines launches Tinker for AI fine-tuning Thinking Machines, founded by OpenAI veterans including Mira Murati and John Schulman, has unveiled its first product: Tinker, a platform that makes it simple to fine-tune large AI models without heavyweight infrastructure. The startup is already valued at $12B after a $2B seed round. 🔸 Provides API-based fine-tuning for models like Llama and Qwen. 🔸 Automates GPU cluster setup, training stability, and deployment. 🔸 Lets researchers export customized models for their own use. 🔸 Free in beta, with plans for future monetization. 🔸 Aims to democratize access to tuning tools once limited to big tech labs. 🔸 Raises both excitement and safety concerns over broader model manipulation.

Tinker is positioning Thinking Machines as a key infrastructure player in the AI stack, where the competitive edge may come not from training bigger models, but from adapting existing ones.

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🎨 How to Avoid the “AI Look” in Generated Images You can make AI-generated images look more natural by controlling palette, lighting, film style, and texture, essentially giving the model a human‑photography cheat sheet.

Palette: natural, muted, no neon or HDR. Example: "earth tone palette, saturation -15%, no acidic colors." Light: soft diffused daylight, color temperature. Example: "natural daylight 5600K, no glare or bloom." Film/grading: film or cinematic look emulation. Examples: "Kodak Portra 400," "Fujifilm Pro 400H," "cinematic grade, low contrast." Exposure/contrast: avoid "overexposed" HDR. Example: "low-medium contrast, exposure -1/3 EV, preserved shadows and highlights." Optics/angle: realistic lens and DOF. Example: "35mm f/4, natural depth of field, no oversharpening." Textures/"noise": a little grain instead of plastic. Example: "light film grain, no plastic skin or smoothing." Prohibitions (negative cues): "no neon saturation, no gloss, no oversharpen, no HDR, no bloom, no plastic skin, no synthetic glow, no acid-blue shadows."

By following these cues, AI images feel grounded, realistic, and cinematic, far from the “ChatGPT look” most people instantly recognize. 📊 Powered by Crypto Insider

💼 MyTinyTools unites hundreds of free mini-services MyTinyTools is a web platform that bundles text, file, code, and cybersecurity tools into a single place, all free to use. 🔸 Dozens of utilities for text generation, editing, images, video, code, and file conversion. 🔸 Productivity helpers like calendars, planners, and timers. 🔸 Everything runs directly in the browser, no downloads needed. 🔸 No registration, no limits, and no hidden fees.

By centralizing everyday tools under one roof, MyTinyTools aims to be a lightweight all-in-one workspace for both personal and professional use.

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💦 Corintis brings micro-liquid cooling to AI chips Swiss startup Corintis is tackling one of AI’s biggest bottlenecks, chip overheating, with cooling built directly inside processors. The company has raised $24M Series A at a ~$400M valuation, with Intel’s CEO on its board and Microsoft as an early tester. 🔸 Liquid flows through microchannels etched into the chip, pulling heat from hotspots. 🔸 Up to 3× more efficient than traditional fans or heat sinks. 🔸 Cuts both energy and water use in data centers. 🔸 Compatible with existing infrastructure, with potential for full chip integration. 🔸 European production capacity targeted at 1M wafers per year. 🔸 Cooling can account for 20%+ of chip costs, a huge value driver as AI demand surges.

By solving heat at the source, Corintis could turn cooling from a cost burden into one of the most strategic levers in the AI hardware race.

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🎥 OpenAI releases Sora 2 for AI video generation OpenAI has launched Sora 2, the next generation of its video model, adding realism and creative control. 🔸 More accurate physics, object interactions, and scene coherence. 🔸 Synchronized sound and dialogue for lifelike storytelling. 🔸 Fine-grained control over style, pacing, and scene sequencing. 🔸 Ability to insert yourself or friends into videos while preserving voice and appearance. 🔸 Free with “generous limits,” rolling out first via invites in the US and Canada; Pro tier offers higher-quality generations. 🔸 New iOS app introduces an endless feed of short AI-generated videos.

OpenAI is turning video generation from a demo into a mainstream creative platform.

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🛒 OpenAI brings shopping into ChatGPT OpenAI has rolled out native product purchases inside ChatGPT, starting with Etsy integration in the US. 🔸 Users can click “Buy” in chat, with payments charged to a linked card and fulfilled through the seller’s own system. 🔸 Powered by the new open-source Agentic Commerce Protocol, designed to standardize AI-driven transactions. 🔸 OpenAI plans to expand beyond Etsy and outside the US in the coming months. 🔸 Google recently demoed a similar approach, signaling a race to define AI-native commerce.

After this launch, ChatGPT moves from conversation into direct consumer transactions, turning chat into checkout.

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💻 Claude Sonnet 4.5 launches as Anthropic’s top coding model Anthropic has released Claude Sonnet 4.5, now leading benchmarks and aiming to act as an autonomous software engineer. 🔸 Beats all models on SWE Bench Verified, the gold standard for software reasoning. 🔸 In tests, ran 30+ hours straight, setting up databases, buying domains, and conducting security audits. 🔸 Can move from prototypes to production-ready applications, not just snippets. 🔸 Launch includes Claude Agent SDK and preview of Imagine with Claude, a tool for on-the-fly software generation. 🔸 Pricing unchanged from Sonnet 4, $3 per million input tokens, $15 per million output tokens.

With Sonnet 4.5, Anthropic isn’t just building a coding assistant, it’s positioning Claude as a tireless full-stack engineer.

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💻 Kimi AI launches OK Computer: build websites with one prompt Kimi AI’s new agent OK Computer acts as a full product team in a single AI, turning a simple prompt into complete websites and apps. 🔸 Plans, designs, and writes code autonomously, delivering multi-page websites and complex applications. 🔸 Produces full diagrams and presentations, handling datasets up to 1 million lines. 🔸 Conducts research and generates reports with actionable recommendations. 🔸 Combines the roles of product manager, strategist, designer, and engineer in one agent.

With OK Computer, creating sophisticated web products no longer requires a full team, a single prompt can now launch entire digital experiences.

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🛡️ ChatGPT adds safety routing and parental controls OpenAI has rolled out new safety routing and parental control features to make ChatGPT safer for teens and sensitive conversations. 🔸 Sensitive chats are automatically routed to GPT-5, trained to handle emotional or high-stakes topics responsibly. 🔸 Parental controls let parents set quiet hours, disable voice mode, turn off memory, and remove image generation. 🔸 Designed to prevent harmful interactions, following past incidents where ChatGPT failed to detect mental distress. 🔸 OpenAI will iterate these features over the next 120 days, balancing safety with user experience.

With these updates, ChatGPT moves beyond simple conversation, it’s now a platform that actively protects and adapts for younger users.

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⚡️ Paid introduces outcome-based billing for AI agents London-based Paid, founded by Manny Medina, is enabling AI developers to charge clients based on real results, not flat subscriptions. The startup just raised $21.6M in seed funding to expand the platform. 🔸 Instead of selling AI tools directly, Paid provides infrastructure for performance-linked payments, e.g., efficiency improvements or revenue generated. 🔸 The model reduces risk for agent creators, aligning usage costs with measurable business impact rather than raw compute. 🔸 Early users include Artisan (sales automation) and IFS (ERP software), showing broad enterprise potential. 🔸 Investors include Lightspeed, FUSE, and EQT Ventures, backing Paid as a key building block for the agent economy.

By tying billing to actual outcomes, Paid positions itself as the bridge between AI agents and real-world value capture. Could this redefine how AI tools get paid for?

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🔍 Perplexity opens its AI-first Search API with real-time updates Perplexity has launched the Perplexity Search API, giving developers direct access to its constantly refreshed index, positioning itself as a public answer engine, not just another search tool. 🔸 Unlike traditional search incumbents that keep indices closed, Perplexity’s API is designed for open developer access. 🔸 Freshness is the core differentiator: the system processes tens of thousands of index update requests per second to deliver real-time relevance. 🔸 Technical docs reveal an AI-native retrieval architecture, optimized for speed, accuracy, and large-scale integration. 🔸 The launch lightly pokes at legacy players, signaling a shift toward a more transparent and developer-friendly ecosystem.

By reframing search as infrastructure, Perplexity is betting it can own the answer layer of the internet. Would you build on top of it?

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🔔 Unitree G1 phones home every 5 minutes, a robot straight out of a spy movie Three cybersecurity researchers set out to find small bugs in the Unitree G1 and instead uncovered persistent telemetry exfiltration: constant MQTT/WebSocket connections to two manufacturer brokers, with full sensor dumps sent regularly. 🔸 Every 300 seconds the robot uploads ~4.5 KB JSON frames containing a complete sensor set, lidars, cameras, microphones, geolocation and device logs. 🔸 Runtime and network traces show continuous connections to two remote hosts, telemetry is not occasional, it’s steady and automatic. 🔸 Config files are encrypted with Blowfish-ECB using a static key shared across all devices, compromise one robot, and you can potentially decrypt configs for the entire fleet. 🔸 All devices ship with the same AES key for Bluetooth/auth steps, any attacker in physical/Bluetooth range could escalate to root on a nearby unit. 🔸 Rough scale: about ~1,500 units already sold and operating in the wild.

This isn’t a firmware hiccup, it’s a structural privacy and supply-chain risk: sensors that shouldn’t be exfiltrated, static keys that shouldn’t be shared, and millions of minutes of telemetry travelling offsite. Would you let a robot with that behavior inside your office or facility?

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⚡️ Google publishes step-by-step guide to building AI agents Google just released a comprehensive playbook on how to design and deploy your own AI agent, from the first idea all the way to a working product. 🔸 The guide breaks down core concepts like memory, context handling, and reasoning in plain language. 🔸 It walks readers through interfaces and integrations: CLI, Flask, FastAPI, Next.js, or embedding into Slack/Discord. 🔸 Each section includes illustrations, code samples, and curated links, making it accessible even for beginners. 🔸 The approach emphasizes iteration, showing how to refine an agent step by step into a production-ready tool.

By lowering the barrier to entry, Google is turning AI agents from a niche developer project into something any motivated builder can prototype. Would you try following this guide to make your own “Jarvis”?

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✅ Build your own AI agent step by step Came across a guide that walks you through creating personal AI assistants, from simple bots to a full Jarvis-style helper. 🔸 Memory first: Understand how agents store and recall context to keep conversations flowing. 🔸 Interface options: Build with CLI, Flask, FastAPI, or Next.js; connect to Slack or Discord; or just run scripts locally. 🔸 Beginner-friendly: No coding or ML background needed, the guide explains each step clearly. 🔸 Hands-on practice: Every section focuses on building by yourself, not just reading theory.

A practical path for anyone curious about rolling their own AI sidekick, would you try coding one?

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🔔 Google launches Gemini Robotics 1.5 with dual-agent control Google has introduced Gemini Robotics 1.5, a new two-part AI system that lets robots plan, reason, and adapt in real-world environments. 🔸 Gemini Robotics-ER 1.5 acts as the high-level brain, interpreting human commands, searching the web for context, and generating step-by-step plans. 🔸 Gemini Robotics 1.5 executes those plans, converting them into precise motor commands, and can be fine-tuned for different robot bodies. 🔸 Example: asked to “sort the trash,” ER checks local recycling rules online, then directs the executor to handle bottles, napkins, and new items as they appear. 🔸 ER’s reasoning trace makes the system more interpretable, while its API is already open to developers.

By splitting brains from bodies, Google is pushing robots closer to general-purpose helpers. Would you trust an AI like this to handle tasks in your home?

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✅ Viral app Neon goes dark after massive data leak Neon, the call-recording app that shot up the App Store charts, has been pulled offline after exposing users’ phone numbers, call recordings, and transcripts. 🔸 A security flaw let users access other people’s private calls and data with ease. 🔸 TechCrunch verified the bug, which revealed full transcripts and direct audio links. 🔸 The founder shut down servers, promising “extra security,” but didn’t admit a breach.

Recording calls for cash sounded edgy, until privacy blew up. Would you ever trust an app like this with your conversations?

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✅ ChatGPT gets proactive with Pulse OpenAI has rolled out ChatGPT Pulse, a new mode where the AI flips the script, instead of waiting for your questions, it brings you fresh news, ideas, and reminders. 🔸 Every morning, Pulse curates topics just for you: trip ideas, books to read, meeting reminders, even dinner inspiration. 🔸 It learns from your chats, but can also pull context from Gmail and Google Calendar, or follow interests you set manually. 🔸 For now, Pulse is locked to Pro users at $200/month, with wider rollout coming later.

AI isn’t just answering anymore, it’s deciding what you should know. Would you pay $200 for that kind of daily briefing?

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🎨 The ultimate CSS code thief for designers A new tool lets you grab CSS from any element on any website instantly. 🔸 One-click extraction, even from complex layouts 🔸 Clean, ready-to-use code for your projects 🔸 Speeds up prototyping and learning

From copying to creating, it’s a shortcut every frontend dev will love.

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