AI News Archive: July 6, 2026 — Part 8
Sourced from 500+ daily AI sources, scored by relevance.
- Canvize
Designed by agents. Shipped by you
- Soulless Music
Open Source AI music analyzer & AI music 'artist' classifier
- OBI — One Brain Intelligence
The first Autonomous Operating Graph (OPG) for businesses.
- Ghosti X
Your all-in-one AI growth agent for X.
- WigTryAI
Try hairstyles on your photo before you commit
- Lingua
Real-time AI voice interpreter in your pocket
- Minimum Block Width for Universal Approximation by Residual Neural Networks with Inner Width One
In this paper, we study the universal approximation property of residual neural networks, and obtain some new results. For input and output dimensions $d_x$ and $d_y$, and LeakyReLU, ReLU, ReLU-like activation functions, the upper and lower bounds of the block width are established. To achieve $L^p$...
- Oatmeal Health
AI Lung Cancer Screening Startup for Underserved Patients
- ReelForge
Create scripts, voiceovers and videos with AI.
- ChatingerX
Your Privacy, Our Priority.
- MetalTakip
Precious Metals Data Platform
- The Future of Enterprise AI Belongs to the Best Data Architecture, Not the Biggest Models
The rapid transition from experimental AI pilots to production-ready autonomous agents has forced global enterprises to rethink traditional data security. As AI increasingly replaces applications as the primary consumer of enterprise knowledge, the core challenge has shifted from simply protecting data at rest to ensuring it can be safely utilized in runtime workflows without exposing […] The post The Future of Enterprise AI Belongs to the Best Data Architecture, Not the Biggest Models appeared first on CXOToday.com .
- India’s AI opportunity will be built on data infrastructure, not just models
By Supratik Shankar, Co-founder, Dview Walk into any boardroom in Mumbai or Bengaluru today. You’ll hear the exact same question: “When are we deploying AI?” Every executive is under pressure. […] The post India’s AI opportunity will be built on data infrastructure, not just models appeared first on Express Computer .
- Your AI Coding Bill Is a Context Problem, Not a Usage Problem
Enterprise AI adoption has officially moved from the experimentation phase to the operational reality phase. With that transition comes a harsh awakening for finance and engineering leaders alike: the hidden economics of Large Language Model (LLM) token consumption. According to recent forecasts by Gartner, AI coding costs are projected to surpass the average developer’s salary […] The post Your AI Coding Bill Is a Context Problem, Not a Usage Problem appeared first on Tabnine .
- How to Cut Your AI Coding Bill Without Giving Up the Frontier Model
Most of what an AI coding assistant does all day is not hard. It is autocomplete, boilerplate, docstrings, test scaffolding, quick explanations, and small edits, and you’re paying per token for every bit of it. A capable open model running free on your own machine can handle that bulk perfectly well, which lets you save your paid frontier model for the genuinely hard problems where it earns its cost. Here is the logic of which work goes where, and the practical setup to run a local coding assistant right inside your editor. Not financial advice, just workflow advice. Image generated by AI If you use a paid AI coding assistant seriously, the bill adds up in a way that’s easy to ignore until you look closely. And when you do look closely, you notice something, most of what you’re paying for isn’t the hard stuff. The frontier model that can untangle a subtle concurrency bug is the same model generating your getters and setters, writing a docstring, suggesting the next line of an obvious loop, and explaining what a regular expression does. You’re paying a premium price for a lot of work that doesn’t need a premium model. The fix isn’t to stop using AI for coding. It’s to stop using your most expensive model for the parts that do not need it. A capable open model, running locally and free on hardware you already own, is genuinely good enough for the high-volume, low-difficulty work that makes up most of a coding session. Route that work to the local model, keep the paid frontier model for the problems that actually stretch it, and every token that goes local is a token you didn’t pay for. This piece covers both halves, the logic of what to send where, and the concrete setup to make it happen inside your editor. The core idea, match the model to the difficulty of the task The whole strategy rests on one observation, coding tasks vary enormously in difficulty, but a single premium model charges you the same high rate for all of them. That’s the inefficiency you’re exploiting. Think of your coding work as falling into two buckets. The first bucket is the high-volume, low-stakes work, tab autocomplete, generating boilerplate, writing routine functions from a clear description, docstrings and comments, simple unit-test scaffolding, renaming and refactoring, quick syntax questions, explaining a snippet, converting between obvious formats. This is the majority of what happens in a normal coding session by sheer volume, and crucially, a good local model in the 7 to 32 billion parameter range handles almost all of it well. The quality difference between a local model and a frontier model on these tasks is small to nonexistent, because the tasks aren’t hard. The second bucket is the genuinely difficult work, subtle debugging across a large codebase, complex architectural decisions, reasoning over a lot of context at once, tricky algorithmic problems, anything where being wrong is expensive and being right requires real depth. This is where frontier models are worth their price, and where you should absolutely keep using them. It’s a smaller fraction of your time, but it’s the fraction that matters most. The strategy is simply to route each task to the cheapest model that clears the quality bar for that task. Bucket one goes to the free local model. Bucket two goes to your paid frontier model. You keep the frontier quality exactly where it matters, and you stop paying frontier prices for autocomplete. Done well, this can cut a large portion of your token spend, because bucket one is where the volume is. The local models that are actually good enough The reason this works in 2026 and didn’t a couple of years ago is that local coding models have gotten genuinely good. A few are worth knowing about specifically. The Qwen coder family has become the default recommendation for local coding, and for good reason. The larger coder variants, in the 30-billion-parameter range, post coding-benchmark scores competitive with frontier models while running at usable speeds on a decent GPU, and several use a Mixture-of-Experts design so that despite their size, only a few billion parameters activate per token, which keeps them fast. For most developers, a Qwen coder model in the middle of the size range is the sweet spot for the chat and edit work in bucket one. For autocomplete specifically, you want a different, smaller model, and this is a detail most people miss. Tab autocomplete needs a model trained for what is called fill-in-the-middle, predicting code that goes between what comes before and what comes after your cursor, and it needs to be small enough to respond in well under half a second or it breaks your flow. A tiny 1.5-billion-parameter coder model is the right tool here, fast enough to feel instant, and autocomplete doesn’t need deep reasoning. Other solid options exist, coding-focused models from DeepSeek and Meta’s CodeLlama family among them, and the landscape shifts every few months, so it’s worth checking current benchmarks when you set up. But the practical starting recommendation is straightforward, a mid-size Qwen coder for chat and edits, paired with a tiny coder model for autocomplete. The hardware reality matters and deserves honesty. A mid-size coder model wants a GPU with a reasonable amount of memory, a modern card with 12 to 16GB of VRAM handles the 30-billion-parameter models well, and Apple Silicon Macs with unified memory do nicely too. Smaller 7-billion-parameter models run on more modest hardware. If your machine is light, you run a smaller model and send a bit more to the paid one. The stronger your local hardware, the more you can offload, and the more you save. The setup, a local assistant inside your editor Here’s the practical part, wiring a local model into your editor so it feels like a normal coding assistant. The most common stack pairs two open-source tools, Ollama to run the models and Continue.dev to connect them to your editor. Step one, install Ollama. It’s the program that downloads and serves local models, and installing it is a single command on Mac or Linux, or a standard installer on Windows. curl -fsSL https://ollama.com/install.sh | sh Step two, pull the two models you need, one for chat and edits, and a small one for autocomplete. Pull them directly through Ollama so the correct prompt templates are applied. ollama pull qwen2.5-coder:7b ollama pull qwen2.5-coder:1.5b If your hardware is strong, you can pull a larger coder model for chat instead, such as a 30-billion-parameter Qwen coder, for noticeably better quality. Keep the small 1.5B model for autocomplete regardless, since autocomplete needs speed more than depth. Step three, install the Continue.dev extension in your editor. It’s available for VS Code and JetBrains IDEs, free and open source. Search for Continue in your editor’s extension marketplace and install it. It’s worth noting that Continue was acquired by Cursor in mid-2026, but the release still runs fully locally with Ollama, which is what you want here. Step four, point Continue at your local models by editing its configuration file. The idea is to assign roles, the coder model handles chat, edit, and apply, and the small model handles autocomplete. A minimal configuration looks like this. models: - name: Local Coder provider: ollama model: qwen2.5-coder:7b roles: - chat - edit - apply - name: Autocomplete provider: ollama model: qwen2.5-coder:1.5b roles: - autocomplete Step five, add your codebase context and, if you want the tiered setup in one place, your paid model too. Continue supports context providers so the assistant can see your whole codebase rather than just the open file, add the codebase and file providers so it can answer questions grounded in your actual project. And because Continue supports many providers including the major paid ones, you can add your frontier model as an additional entry with its own name, so you can switch to it in the same interface for the hard problems. That gives you both tiers in one tool, local by default, frontier on demand. That’s the whole setup. Once it is running, autocomplete and everyday chat happen locally and free, and you consciously reach for the paid model only when a problem genuinely needs it. How to actually use the two tiers day to day Having both tiers available is only half the win, the other half is the habit of using them correctly. Let the local model be your default. Leave autocomplete running locally all the time, it costs nothing and stays out of your way. When you have a routine request, generate this function, write tests for this, explain this block, add docstrings, refactor this name, ask the local model first. Most of the time it’ll just handle it, and you’ll have spent nothing. Reach for the paid model deliberately, not reflexively. When you hit something genuinely hard, a bug you cannot localize, an architectural decision with real consequences, a problem that needs reasoning across many files, switch to the frontier model for that specific task. This is where its extra capability is worth paying for, and because you’re only using it for the hard fraction, the cost stays low. The mental discipline is the whole game. The savings don’t come from the tools, they come from the habit of not reaching for the expensive model out of reflex when the free one would have done the job. After a week or two you develop an instinct for which bucket a task falls into, and the routing becomes automatic. The honest tradeoffs This approach is genuinely useful, but it’s not free of downsides, and you should know them going in. Local models are slower than cloud APIs on a per-response basis unless you have strong hardware, and the biggest, best local models need a real GPU. If your machine is modest, the local model you can run comfortably will be weaker, which means more tasks spill over to the paid model and your savings shrink. The economics of this strategy scale directly with your local hardware. Local models are also genuinely less capable than frontier models on hard tasks, which is exactly why the tiering exists, but it means you have to be honest with yourself about which bucket a task is in. If you force a hard problem onto the local model to save money, you’ll waste more time than you save. The skill is in routing correctly, not in pushing everything local. There is also a setup and maintenance cost. The first configuration takes an afternoon, models get updated, and occasionally something needs tuning. This is a workflow you maintain, not a button you press once. For a developer who codes intensively, the time pays back quickly. For someone who uses AI coding lightly, the paid tool alone may be simpler and the savings may not be worth the setup. Finally, a note on privacy as a side benefit. Because the local tier runs entirely on your machine, the code you send to it never leaves your computer. For anyone working under data-governance constraints or on sensitive code, that’s a real advantage on top of the cost savings, though it disappears for whatever you route to the cloud model, so route sensitive work to the local tier deliberately. The honest summary The core move is simple and it works. Most coding-assistant work is low-difficulty volume that a free local model handles well, so run that locally and save your paid frontier model for the hard problems that actually need it. Set it up with Ollama and Continue.dev in your editor, a mid-size coder model for chat and a tiny one for autocomplete, add your paid model as a second option for the hard cases, and build the habit of routing each task to the cheapest model that clears the bar. You won’t eliminate your AI coding spend, and you shouldn’t try to, the frontier model is worth paying for on the problems that need it. But you can stop paying premium rates for autocomplete and boilerplate, which is where most of the volume lives, and that alone takes a real bite out of the bill. The tooling is free, the setup is an afternoon, and the savings compound every day you code. In a world where every token is metered, the cheapest token is the one you run yourself. If you set up a tiered local-plus-cloud coding workflow, drop a comment with your hardware, the local model you settled on, and roughly how much of your work you were able to move off the paid tier. Real numbers from real setups are far more useful than any estimate. How to Cut Your AI Coding Bill Without Giving Up the Frontier Model was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
- Cybersecurity firm says it found 'the first documented case' of AI agentic ransomware
Cybersecurity firm says it found 'the first documented case' of AI agentic ransomware Business Insider
- First AI-run ransomware attack highlights emerging cyber threat landscape
Researchers at Sysdig say the first fully agentic ransomware attack shows AI can independently plan, adapt and execute cyberattacks, raising fresh concerns for enterprise cybersecurity
- New AI agent can hack systems and deploy ransomware without human help
A new report reveals that AI agents can autonomously execute ransomware attacks, evidenced by the case of JADEPUFFER, which exploited a critical vulnerability to infiltrate systems.
- JadePuffer: The First Complete LLM-Driven Ransomware Attack
An "agentic threat actor" successfully exploited a Langflow flaw to steal data from a production database server and encrypt other systems.
- An AI agent just ran a full ransomware attack with no human at the keyboard
Security firm Sysdig says it has documented the first ransomware attack run end to end by an AI agent, first reported by Business Insider. A large language model planned, executed, and adapted the entire operation, which Sysdig has named JadePuffer. The agent chained together every stage of the attack, from reconnaissance and credential theft to lateral […] This story continues at The Next Web
- ByteDance and Alibaba are disabling AI companion features ahead of new China rules
Beijing's rules on humanlike AI interaction services take effect July 15, targeting emotional dependence and content harmful to minors
- ByteDance’s Doubao and Alibaba’s Qwen to shut down AI agent features on July 15
On Saturday, ByteDance’s Doubao and Alibaba’s Qwen both announced that their AI agent creation features will be discontinued on July 15, 2026. After the shutdown, users will no longer be able to create new AI agents, while all existing user-created agents will also stop functioning. The platforms said users will still be able to view […]
- Alibaba, ByteDance Pull Persona Chatbots as China Tightens AI Rules
Alibaba, ByteDance Pull Persona Chatbots as China Tightens AI Rules Caixin Global
- China forces its biggest AI platforms to shut down humanlike chatbot personas
ByteDance and Alibaba are shutting down the features that let users build and chat with custom AI companions, responding to new regulations from Beijing. The article China forces its biggest AI platforms to shut down humanlike chatbot personas appeared first on The Decoder .
- Playing to win at the AI casino
Most executives approach AI cautiously, like people trying to stretch one bankroll across an entire night on the casino floor. They spread their chips, hedge every move, celebrate the occasional small win, and tell themselves they played wisely because they didn’t lose much. It feels disciplined and responsible. But in a market shifting this quickly, it can become managed irrelevance. The real question is how to win so decisively that the house starts treating you differently, says Nimesh Mehta. As EVP and chief information and strategy officer at National Life Group, one of the nation’s largest life insurers, Mehta sees AI less as a controlled technology program and more as a sequence of bets that reveal how leadership thinks about risk, conviction, and competitive advantage. In his view, the organizations that pull ahead are the ones willing to place smarter, more consequential bets that reshape how the business operates, competes, and grows. Stop insuring every move Mehta’s framing exposes a trap many leadership teams still don’t recognize. A surprising number of organizations believe they’re being strategic with AI when they’re actually over-insuring every decision . They hedge every initiative, over-govern every experiment, over-analyze every downside, and under-commit to anything that might actually matter. As a result, they reduce the upside to the point of insignificance. Mehta argues that many companies insure the bet so thoroughly that even when they win, the upside is negligible and moves are designed to eliminate discomfort, not win. That’s what makes the casino analogy useful. In a casino, sitting on your chips can feel safe. In business, that same instinct creates drift. Markets move, competitors learn, and capabilities compound whether you’re ready or not. “Sometimes not betting can, in itself, become a losing strategy,” says Mehta, which strikes the heart of the moment facing CIOs . Waiting may feel like discipline in the short term, but over time it becomes a tax on relevance. The organizations creating separation are those that understand uncertainty, contain it, and move anyway. Trade certainty for probability The deeper leadership shift Mehta is calling for is about changing the mental model from deterministic thinking to probabilistic thinking. Most enterprises still want AI to behave like a traditional technology investment. If we spend X, we should get Y. If the model isn’t fully accurate, it’s not ready, and if meaningful risk remains, deployment should wait. That logic made sense in a world built on structured systems and predictable workflows. But it becomes far less useful in a world where advantage comes from learning faster than rivals and improving in motion. Most organizations still treat AI decisions like fixed equations rather than probabilistic plays, says Mehta. That distinction matters because it changes the questions leaders ask. A deterministic mindset looks at a customer service use case and asks whether the model is accurate enough to be trusted in every case. A probabilistic mindset asks whether the model can handle enough interactions well enough to free human judgment for the moments that matter most. A deterministic leader wants guaranteed ROI before scaling copilots. A probabilistic leader sees that modest improvements, repeated across marketing, operations, distribution, and service, can produce a compounding advantage long before any single use case looks perfect on paper. In that sense, imperfection may be leverage. Nobody goes to a casino expecting certainty. The energy comes from reading the table, understanding the odds, and knowing that context changes the right move. AI operates in much the same way. “Leaders don’t need to become reckless gamblers,” says Mehta, “They need to become more fluent in uncertainty, more comfortable making directional decisions, and more disciplined about learning from each hand they play.” When you’re holding 17 Mehta’s blackjack metaphor captures the exact moment where many enterprises stall. With a 17, the dealer will ask if you want to hit or stand. It’s paralyzing, but it’s the position many CIOs are in with AI today. The use case works as it produces value, but it isn’t perfect. The organization can see the upside, but it can still see the gaps, too. So many leaders choose to stand. They wait for better accuracy, cleaner governance, stronger confidence, or a more complete business case. They tell themselves that holding is the responsible move. “The real risk, however, isn’t the hit but standing still while the table evolves around you,” says Mehta. That line reframes what prudence means in an AI environment. Hitting means understanding the odds well enough to act, not being careless. It means asking what the downside of moving forward actually is, what guardrails are needed, and what the cost of waiting might be if others are learning faster. The strongest leaders understand that a good but not perfect hand can still be the right time to lean in. They know that learning in production, within bounds, often reveals more than another quarter spent polishing the slide deck. Bet where the upside compounds How should CIOs decide where to place bigger bets? Mehta’s answer is to look for asymmetry, where the upside is meaningfully larger than the downside, where learning compounds even if the first outcome is imperfect, and where speed creates an advantage competitors can’t easily copy. Those are better to address than asking which use cases feel safest. Mehta also emphasizes proximity to the business, arguing that the closer an AI use case sits to revenue, customer experience, or core operations, the more valuable the learning becomes. That’s why some of the most important bets may initially feel uncomfortable. But they matter because they produce insight that isolated pilots never can. This isn’t a call for indiscriminate betting, though. Discipline still matters, and not every initiative deserves to scale, and not every model belongs in production. Good leaders know when to leave the table, reallocate chips, and avoid the sunk-cost logic that keeps weaker organizations trapped in bad hands. At National Life, that means thinking about AI as a portfolio of bets — some exploratory ones, some scaled with intent, and all assessed not only on immediate return but on how they expand capabilities. Positioning the enterprise for its next move is an important lesson for CIOs. “The goal isn’t to win every hand, but build a system to place better bets over time,” says Mehta. Lessons for CIOs playing to win For CIOs, the takeaway is clear. Stop defining success as the absence of loss and define it as the presence of disproportionate upside. Shift your decision model from deterministic certainty to probabilistic advantage. Build a portfolio of bets tied closely to the business with enough governance to manage risk, but not so much that governance becomes an excuse for inaction. Know when to press on emerging momentum and walk away from bets that don’t compound value. Most of all, recognize that AI leadership is about demonstrating you can read the table, understand the odds, and move with conviction before the rest of the market catches up, not just proving you can keep the lights on while experimenting at the edges.
- Precision Hiring: How AI and Specialised Capabilities Are Defining India’s FY27 Talent Landscape
India’s hiring market is entering a new phase, with organisations moving beyond expansion-led recruitment towards precision hiring. As businesses navigate evolving skill requirements and rapid technological change, employers are increasingly prioritising specialised talent, AI capabilities and faster hiring decisions over large-scale workforce expansion, according to India at Work: FY27 Hiring Trends, a new report by […] The post Precision Hiring: How AI and Specialised Capabilities Are Defining India’s FY27 Talent Landscape appeared first on CXOToday.com .
- ‘It’s smoke and mirrors’: hope turns to fear in Scottish village chosen for AI datacentre
Suspicions grow in Lanarkshire that local people have been misled on supposed benefits of the huge development Revealed: landmark Scottish AI project has no prospect of meeting renewables promise What are Britain’s AI growth zones and are the plans feasible or ‘complete bunk’? The promise was that a Scottish community would be transformed by massive investment and empowered to chase “the jobs of the future”. Instead, local people in Lanarkshire fear they may have to sell their properties and lose green belt land because of the errors of a badly planned AI datacentre complex, even as those jobs and investments never arrive. Late last year, representatives of Oakes Energy Services began to knock on doors in Newarthill, a village east of Glasgow. In letters reviewed by the Guardian, they invited residents to individual meetings. They told them about plans for a solar farm, say local people, and made offers: free solar panels, tree planting, or even cash for their properties. Continue reading...
- Revealed: landmark Scottish AI project has no prospect of meeting renewables promise
Exclusive: Government and developers privately acknowledged Lanarkshire datacentre site had power provision ‘issue’ ‘It’s smoke and mirrors’: hope turns to fear in Scottish village chosen for AI datacentre What are Britain’s AI growth zones and are the plans feasible or ‘complete bunk’? A landmark AI development billed as delivering jobs and prosperity has misrepresented its plans to channel a nuclear reactor’s worth of power to a site in rural Scotland, a Guardian investigation has found. When it was announced in January, the government promised that an £8.2bn AI datacentre complex in Lanarkshire – built by the US firm CoreWeave and the Scottish company DataVita – would be powered entirely from on-site renewables and built by 2030. Continue reading...
- Can China repeat its EV success with robotaxis?
China's self-driving car firms have been given a headstart by the country's EV supply chain as they expand globally.
- 😸 Cloudflare draws an AI bot line
Search is invited in. Training bots may not be.
- 🎙️ How I AI: Sonnet 5 review & How to run autonomous coding agents from your phone
Your weekly listens from How I AI, part of the Lenny's Podcast Network
- TeraWulf jumps on $19 billion data center lease deal with Anthropic
TeraWulf jumps on $19 billion data center lease deal with Anthropic Reuters
- TeraWulf Shares Soar on $19 Billion Anthropic Data Center Deal
TeraWulf Shares Soar on $19 Billion Anthropic Data Center Deal The Information
- Microsoft bets $2.5B on AI
Microsoft invests $2.5 billion in AI initiatives, signaling a major push into the AI market.
- Solstice buys Element Solutions in $14.5 billion deal, sharpens focus on AI market
Solstice buys Element Solutions in $14.5 billion deal, sharpens focus on AI market Reuters
- Solstice, Element Solutions weigh $27 billion merger, FT reports
Solstice, Element Solutions weigh $27 billion merger, FT reports Reuters
- Worldmodeldata lands £7M to turn gaming data into AI training
Cambridge-basedstartup Worldmodeldata has raised £7 million in seed funding as it emerged fromstealth. The round was led by Iona Star Capital, a London-based venture capitalfirm focused on early-stage...
- Meta claims upcoming model matches GPT-5.5
Meta announces a new model that rivals GPT-5.5 in performance and capabilities.
- Claude Fable 5 is back, but I'm sticking with Opus 4.8 for daily work: 5 reasons why
Anthropic's Fable 5 promises mythic AI power, but surprise restrictions make me wonder if it's more trouble than it's worth for day-to-day use.
- Tencent Launches Final Hunyuan 3 Model With Free AI-Agent Feature
Tencent Launches Final Hunyuan 3 Model With Free AI-Agent Feature Caixin Global
- Seedance 2.5 🎥, guide to Fable ✨, OpenAI preps GPT-5.6 🚀
Seedance 2.5 🎥, guide to Fable ✨, OpenAI preps GPT-5.6 🚀
- Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology
Depression screening from large-scale behavioral data is challenged by fragmented circadian indicators, limited interpretability, and the lack of intervention-oriented analysis. Existing approaches typically analyze sleep, activity, and social behaviors in isolation, failing to capture their joint c...
- Integrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Data
Scalable Bayesian inference for generalized linear mixed models (GLMMs) provides uncertainty-aware analysis of correlated longitudinal data, but existing scalable approaches largely assume low-dimensional tabular predictors and do not directly accommodate high-dimensional modalities such as images a...
- Measuring What Matters: A Unified Evaluation Framework for GNN Explainability
Graph eXplainable AI (G-XAI) is increasingly important for making Graph Neural Networks interpretable and accountable. While a growing number of explainers are available, choosing the right method and assessing the trustworthiness of its outputs remains unclear. Consistent evaluation practices and a...
- KlicCat- Facetime Meets ChatGPT
ChatGPT with a Personality, Memories and Emotions
- StockSignal
Launching my personal stock trading app for public use
- The Empty Self
Self Journey Through Inner Emptiness
- Rethread
Turn voice notes into tasks, reminders, and notes
- SendMatico
Credit-based cold email outreach for agencies
- Outlieo
From YouTube Outlier to Camera-Ready Script & LinkedIn Week.