Archive
Discover and discuss technology tools
Explore the Tiscuss archive by category or keyword, then jump into conversations around what matters most.
Seedcamp Raises $320M to Expand US Footprint
After 18 years of focusing on Europe, early-stage investor Seedcamp said that it has raised $320 million for its latest fund, which will see it expanding its presence in the United States.
Bluesky Introduces Group Chats, Boosting Community Features
Bluesky's latest feature is group chats, arriving amid a shift in focus on building features for smaller communities.
Enterprise AI Trends at VivaTech 2026: European Focus
While Silicon Valley continues pushing aggressively into large language models and consumer-facing AI products, many European companies are focused on applying AI to complex systems already embedded into everyday life.
Next-Gen Social Apps Beyond Instagram: AI-Driven Alternatives
These newer social apps offer alternatives to Big Tech’s feeds, focusing on interests, creativity, and community.
AI Startups Encouraging Off-Screen Experiences
While the AI fundraising machine keeps breaking its own records, some founders are building in the other direction.  Mirror founder Brynn Putnam just raised money for Board, a startup focused on bringing people together through in-person games and social experiences. Cyberdeck creators are going viral crafting whimsical DIY computers that literally encourage users to touch grass. Unlike the AI-free browser crowd, this doesn’t just feel like backlash, […]
Reid Hoffman Steps Down from Microsoft Board for AI Startup Manus
After a very profitable decade on Microsoft's board, Reid Hoffman is stepping down to focus on his AI drug discovery startup Manus.
Ramp's $750M Raise Boosts Valuation to $44B with AI Focus
Ramp has nearly tripled its valuation over the past year as investors scramble to grab a part of the fast-growing startup.
Meta Unveils Instagram, Facebook, and WhatsApp Subscriptions with AI P
Meta is rolling out paid subscription plans for Instagram, Facebook, and WhatsApp worldwide, while also testing new AI, creator, and business-focused offerings under its broader “Meta One” subscription brand.
AI Labs Focus on Recursive Self-Improvement Challenges
A new crop of AI labs are focused on recursive self-improvement — but the goal is proving elusive.
HMD Partners with Indian AI Chatbot for New Smartphone Launch
The partnership is a potential testing ground for both companies to gauge the appetite for an India-focused chatbot.
Maka Kids Raises $3M for Well-being Focused Streaming App
Maka Kids is building a streaming app for children ages zero to six featuring content designed for healthy development. The startup has now raised $3 million in seed funding to scale its platform.
Apple's Siri Update Focuses on Enhanced Privacy
Privacy will be a major theme when Apple unveils a new version of Siri.
WolfSSL's WolfSPDM 1.2 Stack: Embedded SPDM Focused Requester
WolfSSL's WolfSPDM 1.2 Stack: Embedded SPDM Focused Requester WolfSSL's WolfSPDM 1.2 Stack is a robust solution designed for Secure Device Management (SPDM) dev…
Musk Considered Giving OpenAI to His Children, Altman Testifies
Altman said that Musk's focus on controlling the initial for-profit gave him pause because OpenAI was dedicated to keeping advanced AI out of the hands of a single person, and Altman, with his experience running the prominent startup accelerator Y Combinator, knew "founders who had control usually did not give it up."
Google's AI-Focused Announcements at Android Show
Google unveiled its new AI-first Googlebooks laptops, more agentic Gemini features, vibe-coded Android widgets, Gemini in Chrome, refreshed Android Auto, and more ahead of I/O.
New Multimodal Model Enhances Document Understanding at Lower Cost
Report on a model release focused on lower inference cost and better OCR reasoning.
AI Startup Unveils Secure Enterprise Coding Assistant
Coverage of a new startup product focused on secure enterprise AI coding workflows.
AI Tool: Flowseal Blocks Discord and YouTube for Enhanced Focus
Title: Maximize Productivity with Flowseal Introduction In today's digital era, distractions abound—chiefly from platforms like Discord and YouTube. Enhance you…
AI Tool Momentbymoment.app Revolutionizes Time Management
AI Tool Momentbymoment.app Revolutionizes Time Management In an age where productivity is a prime concern, the emergence of specialized software solution like M…
Kompas VC: Investing in Physical World Startups Amid Geopolitical Turm
Geopolitical turmoil has made venture investing challenging, leading Kompas VC to carve out a niche in startups focused on the physical world.
AI in Medicine: California's Tech-Driven Healthcare Shift
Hi everyone! My journalism professor is making us write a feature article with multiple interviews. The topic I got is the relationship between the healthcare and technology sectors in California. I am specifically focusing on how the push and pull between these two sectors is driving the rapid corporatization of healthcare. My article is supposed to explore how the expansion of tech-driven healthcare solutions, such as digital health, AI services, and venture-backed hospitals, is contributing to a healthcare system that increasingly puts profits over patient care. My draft is due this weekend, but 2 of my interviews ghosted me, so I need people to interview and some more ideas. If anyone is willing to give me their opinions on their experiences of AI in medicine or any ideas in the comments, that would be amazing. If any doctors or those involved in either sector would be open to being interviewed, please let me know! I would love the opportunity!
AI Agents: Identity, Not Memory, Was the Key to Stability
Everyone's building memory layers right now. Longer context, better embeddings, persistent state across sessions. I spent weeks on the same thing. But the failure mode that actually cost me the most debugging time had nothing to do with memory. Here's what it looked like: an agent would be technically correct - good reasoning, clean output - but operating from the wrong context entirely. Answering questions nobody asked. Taking actions outside its scope. Not hallucinating. Drifting. Like a competent person who walked into the wrong meeting and started contributing without realizing they're in the wrong room. I run 11 persistent agents locally. Each one is a domain specialist - its entire life is one thing. The mail agent's every session, every test, every bug fix is about routing messages. The standards auditor's whole existence is quality checks. They're not generic workers configured for a task. They've each accumulated dozens of sessions of operational history in their domain, and that history is what makes them good at their job. When they started drifting, my first instinct was what everyone's instinct is: better memory. More context. None of it helped. An agent with perfect recall of its last 50 sessions would still lose track of who it was in session 51. What actually fixed it I separated identity from memory entirely. Three files per agent: passport.json - who you are. Role, purpose, principles. Rarely changes. This is the anchor. local.json - what happened. Rolling session history, key learnings. Capped and trimmed when it fills up. observations.json - what you've noticed about the humans and agents you work with. Concrete stuff like "the git agent needs 2 retries on large diffs" or "quality audits overcorrect on technical claims." The agent writes these itself based on what actually happens. Identity loads first, then memory, then observations. That ordering matters. When the identity file loads first, the agent has a stable reference point before any history lands. The mail routing agent learned the sharpest version of this. When identity was ambiguous, it would route messages from the wrong sender. The fix wasn't better routing logic - it was: fail loud when identity is unclear. Wrong identity is worse than silence. The files alone weren't enough Three JSON files helped, but didn't scale past a few agents. What actually made 11 work is that none of them need to understand the full system. Hooks inject context automatically every session - project rules, branch instructions, current plan. One command reaches any agent. Memory auto-archives when it fills up. Plans keep work focused so agents don't carry their entire history in context. The system learned from failing. The agents communicate through a local email system - they send each other tasks, status updates, bug reports. One agent monitors all logs for errors. When it spots something, it emails the agent who owns that domain and wakes them up to investigate. The agents fix each other. The memory agent iterated three sessions to fix a single rollover boundary condition - each time it shipped, observed a new edge case, and improved. These aren't cold modules. They break, they help each other fix it, they get better. That's how the system got to where it is. You don't need 11 agents The 11 agents in my setup maintain the framework itself. That's the reference implementation. But u could start with one agent on a side project - just identity and memory, pick up where u left off tomorrow. Need a team? Add a backend agent, a frontend agent, a design researcher. Three agents, same pattern, same commands. Or scale to 30 for a bigger system. Each new agent is one command and the same structure. What this doesn't solve This all runs locally on one machine. I don't know whether identity drift looks the same in hosted environments. If u run stateless agents behind an API, the problem might not exist for you. Small project, small community, growing. The pattern itself is small enough to steal - three JSON files and a convention. But the system that keeps agents coherent at scale is where the real work went. pip install aipass and two commands to get a working agent. The .trinity/ directory is the identity layer. Has anyone else tried separating identity from memory in their agent setups? Curious whether the ordering matters in other architectures, or if it's just an artifact of how this system evolved.
AI's Personal Revolution: Threat to Big Tech's Dominance?
There are many people feeling anxious—rightly so—about their own future because of the impressive advances in AI. If we stop to think about it, five years ago this wasn’t a concern for almost anyone, whether individuals or companies. It was something that appeared “out of nowhere” and caused such a massive disruption that giants like Google and Microsoft had to rethink their strategies. OpenAI has existed since 2015, quietly working in an unusual direction compared to the rest of the industry, and when ChatGPT took off globally, the revolution gained real momentum. Today, there’s a lot of talk about the subsidized costs of AI and how this will be unsustainable in the long run—that the bubble will burst, and so on. And that’s where I disagree: to me, there are smaller projects happening around the world, focusing on things that the big players can’t currently afford to prioritize. One example would be optimizing models or personal hardware in such a way that you could run them on your own computer without needing million-dollar equipment. If a large company were to achieve this, I’d bet on Apple or Nvidia—that is, hardware-focused companies. Apple, in particular, seems very suspicious to me, since it hasn’t made major moves during the AI hype and has remained quite quiet on the subject. Just remember that computers existed long before they became PCs (personal computers). Many people didn’t believe that an average person would ever need a computer at home. And the revolution came when computers became personal and accessible products. To me, something similar could happen at some point—and it could cause significant losses for companies that are currently investing massive amounts of money in expanding data centers to process AI.
AI Golf Coach: FlushedAI Launches on App Store
I am a 9 handicap from LA who spent way too much money on lessons over the last few years. Every coach told me something different. One said my takeaway was flat, the next said I needed more hip turn, a third said my shoulders were fine but my hands were late. I stopped knowing what to believe, and my handicap stopped moving. About a year ago I started building what I actually wanted: an AI that watches my swing, pulls out one specific fault per session, and gives me a drill I can do on the range that night. Not a generic YouTube drill, a drill that matches what it saw in the video. I wanted it to remember what we worked on last time. I wanted it to know when I had actually improved. That project is now FlushedAI. It launched on the App Store this month and we filed a patent on the coaching system in March. What it does: 1. Upload a swing video. The AI pulls the key frames and breaks down contact, path, face, tempo, and body sequencing. 2. It writes you a short summary in plain English, plus 3 drills tied to whatever the top miss was. 3. You log sessions (speed, smash factor, miss patterns) and it updates your focus over time. 4. There is also a map with 24,000+ courses worldwide where you can log sightings with friends and a wagers system for golf bets with your crew (AI scans the scorecard, settles the bet). Things I got wrong along the way: 1. First version used a generic vision model. It was confidently wrong about everything. Lesson: general AI is not a golf coach. We had to fine tune on actual swing footage with a PGA pro labeling it. 2. Tried to replace the teacher. Bad idea. The tool is better as a daily practice partner between lessons, not instead of lessons. 3. Built too much at launch. Shipped the swing analyzer, course map, wagers, and drill library all at once. Should have shipped swing analyzer alone and let the rest follow. Ask me anything. Happy to run a free swing analysis on anyone who drops a video in the comments, no app download required. Also giving out free Premium codes to the first 50 people in this thread who want to actually use it. Not trying to sell anything here. Mostly curious what the crowd thinks is missing in the current crop of swing apps.
Apple's Hardware Focus Under New CEO John Ternus
John Ternus, Apple's incoming CEO, is a hardware guy, signaling Apple may be putting devices back at the center of its strategy.