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Not all mandates are shortsighted and dumb. A mandate is a way of putting organizational muscle behind a decision. They force people to have tough conversations about what tradeoffs to expect and what success looks like. Most big transformation projects end up needing mandates.
When anyone can build almost anything, feasibility stops being the constraint. The questions that still decide whether you have a business are the old unglamorous ones: Will they pay, can you reach them, and does the problem actually hurt? AI made building cheap and made those assumptions the whole game.
Hard-tech startups with accessible technology often see rapid replication due to idea saturation and active founder interest.
MWM makes an annual revenue of around $60 million with no hype or headlines, just consistent wins.
An AI crash could still leave behind power plants, data centers, open models, and a workforce trained to work with machines. Overbuilding is how new infrastructure usually gets installed. The crash only opens the door - the rebuild depends on who already owns the compute, models, and tools when prices reset. Spread the means, not the proceeds.
An ontology is a formal, explicit specification of a shared conceptualization. It is an agreement about what things exist in a domain, how they relate, and what we call them. As AI agents become the primary consumers of data, we need a layer that explicitly describes what things mean, how they relate, and what rules govern them. Structure knowledge and make it accessible, and the market will sort out the labels.
Recruiting feels perfectly aligned with AI capabilities because, at its core, it's mostly a data problem. This article looks at the state of the AI recruiting industry. Every AI recruiting company seems to run into the same problems: recruiting isn't very frequent, causing natural user churn; companies can use multiple platforms at once, so there's no loyalty or lock-in; a data moat is hard; and the long-term winner needs to become part of the broader recruiting window to scale. The space is still early, and markets can be unlocked with new capabilities.
'Talent' and 'Talent engineering' are two new roles that have recently emerged. Frontier labs, VCs, and startups are hiring for both roles. A 'talent' is a person who is great at attracting smart people and has deep relationships with all of them. A 'talent engineer' is someone who loves to build people tools and is also a great engineer. The highest talent density companies now hire based on a blend of talent and talent engineering rather than using the traditional model of recruitment.
You can't out-onboard a customer who only needed you once. 10% monthly churn means rebuilding 70% of your user base every year. The biggest lever is category choice, because a daily marketer workflow retains nothing like a one-time birthday-card job. Blended churn hides the split between sticky cohorts and tourists, so the work is steering ICP, onboarding, and acquisition toward the people who come back. Annual plans, dunning, and win-back flows help at the margin, but choosing a repeat-usage customer is the whole game.
A lot of founders are describing their company with the wrong game board. PLG, enterprise trust, marketplace liquidity, and liquidity financing each have different rules, clocks, and ways to die. Name the win condition first, then the feedback loops and constraints that follow from it. Strategy gets sharp the moment you can name the game everyone else is misplaying.
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TLDR reaches 7 million professionals who read daily to stay current on industry news, emerging tools, frameworks, and best practices. Connect with developers, PMs, founders, and tech leaders who are actively seeking and evaluating solutions [10] that improve how their teams work.
X has released a Model Context Protocol (MCP) server that lets AI tools communicate with the X API. The new MCP doesn't add new capabilities beyond what X's API already offers - it just makes them easier to use for AI applications. The integration turns X into an information network filled with real-time data to retrieve and analyze. The MCP tool is not compatible with X's Write API endpoints, so it can't be used to post autonomously.
Claude Sonnet 5 can make plans, use tools, and run autonomously for long periods. Its performance is close to that of Opus 4.8 but at lower prices. Safety assessments show that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer to use in agentic contexts. It has a much lower ability to perform cybersecurity tasks than current Opus models. Claude Sonnet 5 is now available across all plans.
Nano Banana 2 Light, also known as Gemini 3.1 Flash Lite Image, is the fastest and cheapest Gemini image model. The model produces better results than previous generations, but it still makes some spelling mistakes. This post shows what it produced when prompted for a Where's Waldo-style image.
AI has created a new type of user that is rapidly consuming an expanding share of software. Agents intuit and compose software instead of navigating it. They care purely about capabilities and whether they can be invoked and chained reliably. The best product for an agent is just the best product for a developer - software companies need to start thinking like developer tools companies.
High-frequency trading firms succeeded by developing systems that effectively extracted predictive signals from market data at millisecond timescales. Companies are now applying similar approaches to the physical world, creating digital representations of industrial processes to optimize financing and trading of physical assets. The focus is on developing the "reality stack" to turn physical signals into actionable financial intelligence, reshaping markets and investment strategies.
Marc Andreessen argues that the primary obstacles to AI-driven productivity are institutional and infrastructure-based rather than technological.
While liquidity is heavily concentrated among a few top-tier names, structured company-run tender offers are increasingly utilized to distribute capital on a recurring basis without disrupting 409A valuations.
An Omnilux LED face mask and Dyson's "AI-powered British strawberries" explain more about growth than another prompt hack.
AI agents are changing how software gets discovered, recommended, and implemented. They are increasingly influencing implementation and recommendations, giving open-source and API companies a stronger distribution advantage. Agents need software they can understand, test, and use. Open source provides context, and APIs, SDKs, and integrations give them a way to act.
As venture firms grow into massive asset managers, they become structurally incapable of pursuing smaller opportunities, creating a split between capital agglomerators chasing trillion-dollar outcomes and smaller investors serving the increasingly neglected long tail.
People want to see the human behind the product. They want to see their faces, how they think, and the choices they agonized over. People are hesitant to build in public but this is usually due to misconceptions about what that actually means. This post provides four examples of what 'good' building in public looks like. It also dispels some of the misconceptions that may be stopping founders from building in public.
Replacing a traditional contact form with an AI inbound agent helped book 614 qualified meetings across 2.25 million website sessions and 402,000 interactions without adding sales headcount. Beyond answering questions, the agent routes leads based on close-rate data, runs automated re-engagement campaigns, and manages discounting within predefined guardrails to capture revenue that would otherwise be lost.
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Even simple finance tasks take multiple steps. [8]Mercury Command is AI built directly into your account that eliminates that. Say "pay this invoice" or "freeze that card" — and Command does the work. You approve every action. No dashboards, no exports, no friction. Try
Notion Database allows developers to continuously upsert external records. Notion Agents can use custom tools to generate assets, query live data, and hit any API. Notion workflows can be triggered from anywhere using webhooks from any app. Notion Workers are hosted on isolated sandboxes managed by Notion. Developers can use external agents or custom agents within Notion.
OpenTag enables users to run AI agents inside Slack. It is like having Claude in the workspace, except open-source and self-hosted. OpenTag is built on CopilotKit's open SDK for chat-platform agents. A video of the tool in action is available in the repository.
Not all companies have a moat straight away. Some moats need to be earned. It can take years to build scale, brand, networks, and infrastructure. The moat is still real, even if it shows up later.
The US government now effectively decides who gets access to the newest frontier model. OpenAI's GPT-5.6 is rolling out consumer by consumer under government approval, right after Anthropic was forced to pull a model entirely. This quietly turns access and governance into the scarce thing, just as Chinese labs ship their best models openly to anyone. If it holds, where you can even get capability starts to matter as much as how good it is.
AI makes the minimum viable size of a high-output team smaller than it used to be.
The complexity and limits of AI models make building software both easier and harder.
Be yourself, and be helpful to others.
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Every finance task takes multiple steps. Check runway? Find the right report, cross-reference your burn, interpret the graph. Pay a contractor? Track down the invoice, navigate to transfers, fill in the details. Freeze a card? Dig through settings.
_*Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC._
The one-person company is having a real moment. Solopreneurs clearing $1M a year doubled from 2023 to 2025, and roughly three times as many crossed $5M and $10M, and it isn't fraud. The same surge shows up across countries. Newer cohorts ramp faster too - 2025 sign-ups hit $1M about 30% quicker than 2023 and three times quicker than 2019.
Traditional experimentation had its limits, but some of them made sense because product development was slow. Most experiments focus on pulling up revenue. They don't deliver incremental progress. Things are now different, so founders should skip minor optimizations, take bigger swings, and let tests run longer.
As foundational model access commoditizes pure software functionality, early-stage technology startups are pivoting toward proprietary data loops and deeply embedded operational workflows to secure defensible moats. Successful teams are focusing heavily on capturing high-fidelity enterprise interaction data that cannot be scraped or simulated by generalized frontier systems.
Technology analyst Tanay Jaipuria broke down the primary market map split emerging within the agentic software landscape, contrasting end-to-end applications against backend infrastructure tooling. While bespoke vertical agents capture high initial contract values by replacing human service labor, infrastructure platforms gain long-term defensibility by providing essential orchestration layers.
Cal AI went from nothing to the number one health app in 18 months on the back of influencers, and this is the actual playbook. Judge creators on their baseline views and a live comment section, not follower count, and pay flat rates so a viral video never costs you more. The money is in mid-sized creators, and the counterintuitive part is that you're renting the audience, not the influencer, which is why a TikTok dancer outsold UFC fighters for a calorie app. Four people ran hundreds of partnerships by automating the entire pipeline, because the only moat left is speed.
Rippling Data Cloud is a new suite of products that aggregates data from across companies into Rippling, connects it to worker identity, and makes it available for analysis, visualization, and action. It preserves and enriches data context to enable precise and accurate answers to business questions. The data stack includes data connectors, transformations, visualizations, AI-powered analytics, and inbound Zero-ETL. Rippling Data Cloud, together with Rippling AI, allows founders to better understand company performance dynamics across sales, engineering, and operations using only a conversational interface.
Haystack is an AI orchestration framework for building production-ready LLM applications in Python. Its transparent architecture lets developers experiment, customize deeply, and deploy with confidence. Haystack can be used to build scalable RAG systems, multimodal applications, semantic search, and more. It gives developers explicit control over retrieval, routing, memory, and generation so developers can design modular pipelines and agent workflows.
Agentic AI changes the unit of knowledge work from single interactions to delegated, long-horizon tasks. The ability to operate independently for long periods while orchestrating tool calls, interacting with environments, and iterating towards solutions makes agents one of the most powerful AI tools for work. Agentic tools expand what individual workers can do. They lower the cost of moving across task boundaries and help workers do adjacent work that used to require more specialized technical support.
VCs follow in flocks into the hot categories. Founders without heat in those areas get ignored. This doesn't mean they have a bad business. It just means the business isn't interesting to VCs right now.
Anthropic outlined a production framework detailing structural design patterns required to optimize collaborative workflows between human operators and autonomous agents. The design principles emphasize continuous state synchronization and explicit handover protocols to ensure seamless execution across long-horizon business logic tasks.
Everyone's arguing about "loops," but almost no one can define one.
By proactively removing administrative roadblocks before launching a round, founders can significantly compress closing timelines and maintain critical competitive momentum between syndicates.
A quiet antidote to the opportunity-cost panic so many young founders carry.
Being inspiring at work has nothing to do with rah-rah energy.