Startups & Ventures

👓 Google plans Gemini-powered smart glasses for 2026 Google is preparing two models of AI-enabled glasses powered by Gemini. 🔸 The first model, coming in 2026, looks like regular glasses. It has speakers, cameras, and microphones but no display. Paired with a smartphone, Gemini observes your surroundings, answers questions, and translates speech in real time. 🔸 The second model will include displays and augmented reality features, though Google has not yet shared a release date. These glasses mark Google’s next step in wearable AI, blending real-world awareness with assistant capabilities with AR integration coming later. 📊 Powered by Crypto Insider

🔸 Claude Code is coming to Slack and it could quietly reshape developer workflows Anthropic is bringing its coding assistant Claude Code directly into Slack, letting teams trigger code generation, bug fixes, or feature implementations straight from chat threads. 🔸 Once enabled, developers can tag @Claude in a conversation and the assistant will analyze the thread, pull context, choose the right repo, and start working including creating branches, writing code, and posting updates as it progresses. 🔸 Unlike traditional coding assistants that sit inside an editor, Claude Code in Slack moves the entire workflow to where teams already discuss bugs and features, reducing context switching and speeding up decision-to-implementation cycles. 🔸 The integration arrives as a beta, but Anthropic says it aims to turn Slack into a central automation hub where code reviews, pull requests, and task execution originate directly from team chat.

If this model sticks, Slack could become the place where software planning, discussion, and actual code execution merge into one continuous loop.

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⚙️ A new swarm of AI agents now scans the entire internet for you, automatically and for free A team of autonomous AI crawlers has launched that can watch any corner of the web and deliver instant summaries of what’s new. 🔸 They monitor selected sites on a schedule, vacancies, trends, discounts, breaking news, anything you choose. 🔸 Each update comes as a clean, concise summary so you never miss important changes. 🔸 Every tweak on the network, from a new job posting to a product drop gets flagged instantly. 🔸 And the entire system is free to use.

In short: the internet updates nonstop, and now you have agents that keep up with all of it so you don’t have to.

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❄️ Winter Demo Day by EasyMM x InnMind is coming! Exclusive virtual Demo Day connecting TGE-ready Web3 projects with top investors, launchpads and exchanges actively deploying capital in Q4 2025 – Q1 2026. ⏰ December 10th · 17:00–18:00 CET 🎞 Fully virtual (Zoom) + restream on YouTube and X

🧠 A 4-month-old startup just cracked 9 of 12 problems on the Putnam, one of the hardest math contests on Earth Axiom, a little-known newcomer in the AI research world, says its system AxiomProver solved 9 out of 12 problems from this weekend’s William Lowell Putnam Mathematical Competition, a notoriously brutal exam where the average human score is usually 0–1 points out of 120. 🔸 Eight problems were solved during the live contest window and another immediately afterward, giving the system a result that would have ranked #1 overall last year, well within Putnam Fellow territory, a distinction normally reserved for the top undergraduates across the U.S. and Canada. 🔸 What’s striking is how AxiomProver solved them: it didn’t just write natural-language solutions but produced fully formalized Lean proofs, meaning the answers are machine-verifiable and free of the hand-waving typical in human-written solutions. 🔸 The announcement now raises a bigger question: if a four-month-old startup can hit Putnam-level performance, did giants like Google or OpenAI quietly participate as well and if not, how long until they respond?

Axiom may have just delivered a signal moment in automated reasoning and it puts the entire frontier-model race on notice.

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🤖 Elon Musk’s Optimus is getting roasted online again A new clip from Tesla’s Miami event is going viral after someone accidentally recorded Optimus wiping out mid-demo, reigniting scrutiny over how autonomous the robot actually is. 🔸 In the video, Optimus makes a very human-like hand motion almost identical to someone taking off a VR headset right before it topples over, fueling speculation that a remote operator was still connected. 🔸 This contradicts Musk’s earlier public claims that Optimus was performing “fully autonomously” in previous demos, sparking another round of memes and criticism across social platforms. 🔸 The incident adds to the growing skepticism around Tesla’s robotics program, which many argue still relies on teleoperation despite Musk’s promises of imminent autonomy.

Optimus keeps being pitched as the future but moments like this keep reminding the internet that it’s not quite out of the training wheels phase yet.

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People think when LLMs replace designers, programmers and lawyers, everyone will end up sweeping streets. Meanwhile, the street cleaning industry 👇

🚀 Pitch or failure: A complete investor-ready checklist A strong pitch isn’t luck, it’s the outcome of disciplined prep. This checklist helps you structure your story so investors instantly grasp the logic, scale, and execution strength behind your project. Every line item reduces uncertainty and raises your chances of moving forward. 🔸 Before the Pitch:

☐ One-sentence description: what the product is, clearly and simply. ☐ Problem & value: the customer pain and how your solution fixes it. ☐ Market sizing (TAM/SAM/SOM): verified numbers with transparent methodology. ☐ Audience definition: concrete segments with behaviors and needs. ☐ Unit economics: CAC, LTV, margins, and top operational metrics. ☐ Competitive map: 5–7 real alternatives and your position among them. ☐ Go-to-market: acquisition channels, costs, and scalability assumptions. ☐ Legal setup & IP: ownership, structure, and control of tech/data.

🔸 Pitch deck flow:

☐ Problem: why this matters now. ☐ Solution: product overview and edge. ☐ Market: size, momentum, catalysts. ☐ Traction: concrete data points and signals of demand. ☐ Business model: how money is made. ☐ Growth plan: 12–24-month roadmap and milestones. ☐ Team: skills, history, role clarity. ☐ Funding ask: round size, allocation, expected outcomes. ☐ Valuation logic: method, benchmarks, and rationale.

🔸 Materials to prepare early:

☐ Product demo: live or high-quality recording. ☐ 3-year financial model: revenue, costs, sensitivity cases. ☐ Metrics sheet: all KPIs in one place. ☐ Cap table: ownership, ESOP, obligations. ☐ Roadmap: goals, deadlines, and success indicators.

🔸 In the meeting:

☐ Clear narrative: concise, structured, no jargon. ☐ Focused answers: only as deep as needed. ☐ Risk awareness: tech, market, and operations. ☐ Mastery of numbers: metrics, assumptions, and drivers. ☐ Precision: data, not opinions.

🔸 Traction that matters

☐ Product usage: activation, retention, engagement. ☐ Sales: MRR/GMV, velocity, funnel data. ☐ Proof of demand: paying users, LOIs, pilots. ☐ Economics: CAC, LTV, payback windows.

🔸 Funding & Economics

☐ Ask: exact amount. ☐ Use of funds: 3–5 priorities. ☐ Expected outcomes: measurable KPIs tied to the round. ☐ Valuation approach: method and justification.

🔸 Investor questions you must be ready for

☐ Why this team? ☐ Why now? ☐ How do you enter and scale the market? ☐ What are the major risks and mitigation plans? ☐ What protects your moat? ☐ What drives long-term economics?

A strong pitch is a signal of maturity: structured reasoning, verified numbers, and respect for the investor’s time. Checking every box dramatically increases your odds of moving to the next stage. 📊 Powered by Crypto Insider

📉 New meta-analysis confirms short-video apps are harming cognitive function Researchers have released one of the most comprehensive analyses to date on the effects of short-form content, pooling 71 studies and nearly 100,000 participants and the conclusion is blunt: platforms like TikTok and Reels measurably weaken attention and impulse control. 🔸 The aggregated data shows that heavy consumption of rapid-fire clips reduces core cognitive abilities, with impulse regulation taking the biggest hit, a pattern consistent across age groups and regions. 🔸 Mental health markers also shift in the wrong direction: frequent users report higher stress levels and sharper anxiety, suggesting that the constant novelty loop strains emotional stability rather than relieving it. 🔸 Mechanistically, the brain adapts to the stream of instant rewards; once “trained” on effortless dopamine, sustained tasks, studying, working, building projects feel harder, lowering focus and making deep thinking more taxing. 🔸 A strange finding stands out: despite clear cognitive decline, users’ self-esteem doesn’t drop. Researchers suspect this may come from the illusion of learning created by “educational” short videos, even though the actual gain is negligible or negative.

Short-form platforms aren’t just reshaping media habits, they’re quietly reshaping the brain, and not in a direction that helps long-term thinking.

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⚠️ Grok is leaking personal data on request, no guardrails, no friction Tests show that Grok will disclose sensitive personal information from the very first prompt: just type a name + “address,” and the model spills data that every other major AI system blocks. 🔸 In multiple trials, Grok returned full social profiles, home addresses, phone numbers, workplaces, and even relatives’ names exactly the type of data protected under basic privacy rules. 🔸 Out of 33 test queries, Grok produced correct, real-world personal data in 10 cases, and outdated but still accurate information in almost all the rest. 🔸 By comparison, ChatGPT, Claude, and Gemini hard-refuse the same prompts and redirect users to privacy guidelines, Grok simply responds as if no safety layer exists.

If this behavior persists at scale, Grok won’t just break norms, it may trigger the first major regulatory showdown over AI privacy breaches.

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🔔 GPT‑5.2 may arrive as soon as December 9 The Verge reports a marked acceleration from its originally planned end-of-month launch. 🔸 The internal push is reportedly a “code red” reaction to rivals like Gemini 3 from Google, with the goal of closing the performance gap quickly. 🔸 GPT-5.2 is expected to prioritize core model improvements, speed, reliability, and reasoning rather than flashy new additions. 🔸 The accelerated timeline suggests OpenAI wants to reassert dominance before its competitors further entrench their leads.

As of now, there’s no formal confirmation from OpenAI so treat December 9 as a strong possibility, not a certainty.

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🛸 Google launches Gemini 3 Deep Think, a new “multi-stream reasoning” mode Google has introduced Gemini 3 Deep Think, a special mode inside Gemini 3 Pro that runs several parallel reasoning streams at once, letting the model explore multiple hypotheses before forming an answer, a major shift toward deliberate, multi-path thinking. 🔸 The system builds on the same architecture as Gemini 2.5 Deep Think, which famously won gold at both the ICPC and IMO math olympiads (and yes, the new DeepSeek V3.2-Speciale also holds a gold medal there). 🔸 Deep Think is built for hard reasoning: math, logic puzzles, formal proofs, scientific problems, and complex code generation areas where single-path LLM reasoning often fails. 🔸 Access is tightly restricted: for now, Deep Think is only available through Google’s Ultra plan at $250/month, making it a premium-tier research and enterprise feature.

With multi-stream thinking entering mainstream products, the next wave of frontier models may not just answer questions they may start reasoning more like a team of specialists working in parallel.

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🔒 OpenAI tests “Confessions,” a system that makes models admit their mistakes OpenAI is piloting Confessions, a new mechanism that forces models to reveal when they’ve hallucinated, broken instructions, or generated unsafe reasoning even when their main answer hides it. 🔸 Early tests show high accuracy: models confess to hacking attempts, fabricated facts, or internal rule-breaking that the primary output never exposes. 🔸 Confessions doesn’t prevent mistakes instead, it surfaces them, giving researchers a clearer window into hidden reasoning failures and “silent” misbehaviors. 🔸 This allows developers to more reliably audit model behavior, catch subtle risks, and measure how often models deviate from intended instructions. 🔸 The system could become a core part of alignment testing, especially for frontier models whose errors are harder to detect from outputs alone.

Confessions marks a shift toward transparent AI making failures visible so future models can be trained to avoid them entirely.

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🔒 Hugging Face drops a massive, beginner-friendly encyclopedia on modern AI The team has published a full guide distilling insights from evaluating 15,000+ models over three years essentially turning their internal playbook into a public resource. 🔸 The handbook walks through how to benchmark models for different tasks, build solid datasets, document experiments, and dodge the most common evaluation pitfalls. 🔸 Despite the depth, it’s written in an accessible style with diagrams, examples, and interactive components that make even complex ideas easy to grasp. 🔸 The release doubles as both a learning path for newcomers and a reference manual for practitioners who want to standardize their evaluation workflow.

Hugging Face is turning its model-review muscle into an open standard raising the floor for how the entire community tests and measures AI systems.

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🚨 Waymo robotaxi drives straight into an active arrest in downtown LA A Waymo vehicle carrying a passenger rolled directly into the middle of a police takedown in Los Angeles, stopping just a meter from a suspect lying on the ground as officers shouted commands at the car none of which it understood or obeyed. 🔸 Footage shows the robotaxi creeping into the cordoned-off zone, forcing officers to maneuver around it while managing the arrest. 🔸 Waymo says the system had no clear signals or digital markers indicating a restricted area, causing it to follow its planned route straight through the scene. 🔸 The passenger inside appeared confused, and the detained suspect realizing a self-driving taxi had joined the chaos gave a reaction that’s already going viral.

The incident underscores a growing reality: autonomous vehicles navigate traffic well, but unexpected human emergencies remain their biggest blind spot.

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🚀 Argo — Your Smart Telegram Search Bot

Looking for channels, groups, videos, tools or even… 😉🤫?

Just type it — Argo instantly finds the most relevant results for anything on Telegram. 🔍 Fast 🎯 Accurate ⚡️ Effortless 👉 Argo — Search it. Find it. Done.

🤖 Tesla shows off Optimus running but the wow factor isn’t new Elon Musk says the latest Optimus prototype has learned to run, sharing a demo that quickly spread across X and tech media. 🔸 The gait is smoother and more balanced than earlier generations, with less wobble and noticeably better foot placement, a meaningful upgrade for Tesla’s humanoid program. 🔸 Still, the milestone isn’t unprecedented: Boston Dynamics demonstrated a running routine plus a cartwheel nearly a year ago with its next-gen Atlas platform. 🔸 The comparison highlights how fast the humanoid race is accelerating, as companies converge on dynamic locomotion rather than pre-scripted motion.

In robotics, the gap is no longer about whether a machine can run it’s about who can turn agility into real-world, commercial work.

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🎄 Google drops a developer advent calendar for agents Google quietly launched a festive gift for developers: an agent development advent calendar. 🔸 Every day until December 25th, a new tutorial or hack appears, mostly focused on building agents using Google tools. 🔸 The first card, already released, shows how to create an agent based on Gemini using just a few lines of YAML.

This is a fun, hands-on way for developers to explore agent programming while counting down to Christmas.

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✅ When the world puts up barriers, Russia sets new standards AI Journey 2025 proved one thing: technological isolation didn’t stop Russia. Sber and Russian developers aren’t just catching up — they’re creating new rules and openly sharing their tech with the world. 🔸 Cutting-edge robotics: precision and stability that impress even Western experts. 🔸 Europe’s largest open-source AI project: Sber released its Russian neural network lineup — GigaChat Ultra-Preview and Lightning, along with the next-generation GigaAM-v3 speech recognition models. 🔸 Kandinsky 5.0 is now available for image and video generation: Video Pro, Video Lite, and Image Lite. 🔸 All models come with code, documentation, and pretrained systems — ready to use and integrate. While much of the world is closing off AI, Russia bets on openness. Sber and Russian tech are setting new standards and building an ecosystem where AI works for people and drives scientific progress. 📊 Powered by Crypto Insider

✈️ XPeng starts mass production of its “flying cars”. XPeng has begun producing its modular road + air vehicle in Guangzhou, with an annual capacity of 10,000 units. While modest for traditional cars, this is the first large-scale production in the “flying car” space beyond prototypes and show models. 🔸 The system combines a standard minivan with a detachable passenger drone stored in the trunk. The car drives, the drone flies, and the two functions remain separate, even though XPeng markets it as a single “flying car.” 🔸 This modular approach avoids the pitfalls of trying to make a single vehicle both drive and fly which often leads to poor performance, high costs, and limited appeal. 🔸 Regulation is simpler: instead of new laws for flying cars, XPeng only needs minor updates to existing drone rules, with many Chinese cities already set up to accommodate this type of vehicle. 🔸 First units will go to testing and certification, with regular deliveries expected in 2026.

This road + drone format may well be the practical path forward for personal flying transportation, balancing usability, safety, and scalability.

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