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The API for GPM-5.3 is now available. Z.ai plans to make the model's weights openly available, but it has yet to set a release date. API pricing remains unchanged from GLM-5.2, so developers gain substantially stronger coding and long-horizon agent performance without paying more.
AI is like an inexhaustible IQ machine. Getting the same level of energy on demand from a senior or principal engineer is basically impossible. Now, that level of energy and intelligence is available on tap. Anyone can just get a gateway and pick any model in the world, and they basically have unlimited IQ.
An audit of 50 top SaaS companies looked at how each one positions against the foundation models their own customers now ask about. 34 have shipped MCP servers or connectors, so the models route through their product instead of around it. Only 12 mention Claude anywhere on their own domain, and just 4 publish a build versus buy cost comparison for prospects tempted to roll their own. Zapier connects 9,000 apps over MCP, while Intercom hands enterprise buyers a 20-question checklist for evaluating AI agents against a raw model.
Co-founder searches should combine matching platforms, trusted networks, VC referrals, and in-person communities while prioritizing exceptional, aligned people.
AngelList is evolving from a fund-administration platform into private-market infrastructure spanning banking, trust, transaction execution, and broader investor access.
A solo builder let AI agents run his product development for two years.
AI-powered apps now make up 25% of subscription apps, generating 41% more revenue than others within the first year.
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Runway doesn't care about your org chart. Every domestic hire is dollars that could've bought you six more months to hit product-market fit. Connext [5] builds vetted offshore teams for startups that need senior talent without the senior-market price tag: * Zero upfront costs to get started * Positions filled in 21 days on average * Payroll and compliance handled through Employer of Record * 98% client retention over 10 years Most founders save 70% per hire without losing quality. See how it works [6] or talk to their team [7].
Labs are shipping smarter models faster than ever, but 84% of tokens on OpenRouter are not state-of-the-art. The most popular models deliver about 77% of frontier models at 2.5% of Claude Fable 5's price. Frontier models are still winning on software architecture and security design, but application deployment is optimizing price over performance.
Startups are now competing for dollars with every other tech asset in the world. Being the best company in a category is no longer enough. Founders are never going to change an investor's mind if they've already considered an idea and rejected it. Only novel ideas stand a shot.
The Bay Area dominates access to venture capital. AI is radically lowering the barrier to build and scale startups. The same strategies applied in Silicon Valley to other geographies could be interesting. The global market has much less competition and more opportunity.
A GTM engineer focuses on optimizing and strategizing go-to-market systems, leveraging tools like Clay and Unify. Arda's founder, Kyle Henson, shares that hiring this role arose from a pipeline failure when one key employee was on leave, highlighting the need for a systematic approach. Candidates underwent practical challenges without clear instructions, allowing them to demonstrate problem-solving and initiative, ensuring they're more than just order-takers.
In surveys, only 18% of early-stage teams had 70%+ of their reps hitting quota, and a majority were sitting at 20% to 40% attainment or worse. Inside those averages was a group of reps who closed essentially nothing all year. Mediocre reps are an output of the system, not an input. Companies need extreme product-market fit, tight processes, and substantial support for reps to succeed.
By the time a product is ready to go to market, it is largely a given input. Launch broadly, then reverse-engineer, or do the digging first and use the data to find the segment most likely to convert in the next three to six months. This article looks at how to do market research. It covers market segmentation, segment scoring, research methods, the ECP prototype, and the GTM roadmap.
Meta treats the rest of the consumer internet like a giant external product lab. It understands what gets people to create, share, connect, consume, come back, and generally spend more time with their products. When other companies spend years and a bunch of money and discover a new behavior that people clearly like, Meta takes notice. Every software company is going to need to get much better at doing some version of this as software is becoming too easy to build. A lot of software scarcity used to come from implementation, but that is changing fast.
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Ads get ignored on social media but not in TLDR! Reach developers, PMs, marketers, founders and other tech leaders where they actually
Google has introduced Custom Agents in Antigravity 2.0 and the Antigravity CLI, with the Antigravity IDE following shortly. Custom Agents are specialized, file-based configurations that define a particular role with its own scoped instructions, tools, and constraints. The system keeps users' active contexts clean, minimizes token overhead, and gives them a predictable partner for specific tasks. Custom agents don't replace skills and dynamic subagents - they just provide even more customizability for another level of optimization.
Power complex coding agents with post-trained open-source model and cyber defense capabilities.
TLDR is hiring a GTM Engineer to join our Applied AI team and own our AI-native GTM stack. We're looking for someone comfortable building AI agents and working with HubSpot. Click here to learn more [19]!
If you've been following along, you know model routers are everywhere right now. A grey market is running the same play without permission. Founders are getting offers for OpenAI and Anthropic usage at 40% to 50% below list price. One broker claimed access to $100,000 of daily spend and routed requests through its own endpoint instead of handing over provider keys. Public marketplaces list credits across cloud and model providers at discounts reaching 80%, while Telegram and founder forums supply smaller lots. The report estimates tens of millions of dollars in credits are for sale.
The transition from bootstrapped startup to major retail player is the ultimate test of sustainable growth for many founder-led brands. This means taking on more debt or venture capital for some. For others, it may mean bringing on private equity investors to shoulder the costs. This podcast discusses the strategic decisions behind that evolution.
Demand exists independently of supply. It is the factor that determines whether a startup's product is relevant or not. Demand describes 'what wants to happen'. A founder's job is to find out what wants to happen but is currently blocked from happening, and then simply enable that thing to happen.
Short feedback loops make failure cheap.
Most dark-mode toggles make users pick between light, dark, and a confusing third option that follows the system.
You can cut engineering by an order of magnitude, and a product will still ship, but you can't compress a relationship, which is often rated in literal touchpoints, sometimes in real life.
Products are not bought - they are hired for specific jobs.
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AI productivity gains depend on everything being neatly documented in markdown files. That's not how most businesses operate. Crucial context is scattered in notes, conversations, and undocumented processes. Agents burn a bunch of tokens guessing this information - from scratch, every time. Computer, by DevRev [5] fixes that with one shared memory your whole team works from. What anyone learns, everyone can use, instantly.
✅ Proven 48% more accurate than vanilla Claude.
Processors are currently scarce because of the same bottleneck: the high-bandwidth memory and the advanced packaging that binds it to the die are capacity-limited across the entire industry. This can't be fixed with a bigger cloud budget or a patient retry loop. This constraint isn't clearing anytime soon and is likely a permanent condition. Businesses need to build for this reality rather than waiting for the shortage to end.
No industry leader has created a program or certification for AI fluency yet. It's difficult for individuals and hiring managers to understand whether they're doing cutting-edge work or are part of the status quo. Building a portfolio of proof is really important at this stage. Do the work in public, talk about what you're building, and collect third-party proof and outcomes wherever you can get them.
PatronView is a donor-research database with 1.5 million profile pages built from public records. In one week, its server served 1.28 million pages while analytics counted only 5,977 pageviews. That is 214 invisible loads for every visible one. Claude-SearchBot crawled 35,000 pages for every visitor it sent back. Amazon crawled 117,000 pages a day and sent none. Cloudflare's bot detector added 2.875 seconds and cut mobile Lighthouse to 58. Removing it restored 99, then an Azure scraper pulled 23,000 pages in an hour. The founder now judges crawlers by pages taken per visitor returned.
Applied AI companies sell completed work, making the harness, not the model, the core product: tools, integrations, persistence, orchestration, oversight, and evals close the gap from 70% to 98%+ accuracy. Durable advantage comes from compounding knowledge, hardening, and evaluation loops.
One engineer now runs six or seven AI-agent sessions on the same coding task. Some explore, others build competing versions, and more review and test. Things got faster, but his job slowly became skimming and judging answers instead of forming his own. So he added one deliberately slow habit. He reads dense nonfiction and writes about it without an LLM. As more research, code, and analysis move into agent queues, keep one place where you still have to start with a blank page and decide what you think.
TLDR is hiring a GTM Engineer to join our Applied AI team and own our AI-native GTM stack. We're looking for someone comfortable building AI agents and working with Hubspot. Click here to learn more [14]!
Google has released Gemini 3.7 Flash, three weeks after Gemini 3.6 Flash. Until December 31, API rates are $0.75 per million input tokens and $3.75 per million output tokens. The rates double on January 1. The offer gives developers a defined testing window.
ChatGPT's new Computer History feature creates a searchable timeline from activity across selected apps and websites. The feature is off by default and must be enabled by each user. Computer History replaces the Chronicle research preview. While Chronicle relied on screenshots, Computer History records interaction events exposed through macOS accessibility features.
OpenAI has added three new features to its ads platform. The platform will now automatically adjust bids to get the most results from advertisers' budgets. A new Eligible Platforms dropdown allows advertisers to individually deselect ad platforms. There are now new ways to include URL parameters for third-party tracking.
A sales consultant advised a portfolio company with a greenfield market to focus on one vertical and target large enterprises. The strategy involves attending conferences, creating vertical-specific content, and landing key industry leaders as references. Once established, the company should expand into related verticals to leverage their initial success.
Building a business moat involves solving the hardest core problems, not just relying on Helmer's seven lagging indicators. The leading indicator for a defensible business is solving high-effort, high-risk problems directly tied to core user value. Fear of uncertainty often deters entrepreneurs, but confronting difficult problems can create a sustainable competitive advantage.
Stripe studied one million "product not received" disputes to learn what evidence actually wins.
Mellow Sleep sells pillows and comforters on TikTok Shop, where bulky products once looked like a bad fit.
US automakers made cars safer and longer-lived, while like-for-like prices fell roughly 45% after inflation.
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Every purchase that needs your approval waits on you. Every missing receipt is a follow-up you have to send. Mercury Spend fixes that at the source. It's expense management built into Mercury*. Give your team and agents cards with limits you set once, and the policies hold on their own. Receipts scan in from Gmail or arrive by text. Cards with overdue tasks pause until they're resolved. Setup takes minutes. No sales call, no implementation team. Issue your first card and set your first budget in the same session.
The debate between open vs. closed AI models revolves around innovation's direction rather than its level, with open models fostering broad experimentation and closed models maintaining control. Open models encourage widespread tech adoption and specialization, leveraging lower costs and complementary asset synergies.
In the first half of 2026, 195 companies became unicorns, surpassing the entire 2025 total. Robotics and AI sectors led this surge, while notable companies like China-based DeepSeek and Seychelles-based OKX reached valuations of $50 billion and $25 billion, respectively. The US and China were dominant, with fast-follow funding rounds rapidly increasing valuations, particularly in AI and semiconductor startups.
Serial processing is better for tasks that must be completed in a specific order and best performed by the CPU. Parallel processing is better for tasks that can be divided into many parts and done simultaneously and is best performed by a GPU. Many problems benefit from the fusion of both approaches, where the main problem is broken into many smaller independent problems solved in parallel. The solutions of the smaller problems can then be integrated serially to create the complete solution.
Organic marketing works best when founders define one specific buyer, study their language and communities, then design content packaging around behavior. Test hooks and formats, read retention signals, repurpose winning ideas by platform, and automate repetitive research and packaging.