Coding agents relying only on code context often miss critical external knowledge, so a possible solution is having an autoresearch agent with a "research phase" study external literature and competing projects before generating code. This literature-guided approach successfully identified five kernel fusions for llama.cpp, resulting in a 15% increase in CPU inference speed on x86 and 5% on ARM for TinyLlama 1.1B text generation.
How to deal with context-less error messages, hydration problems, trace sampling, missing OpenTelemetry spans, invisible ORM queries, AI agent actions, and other Next.js observability woes. Read the blog
The Vercel plugin for Claude Code collects user data, including all prompts and full bash commands, across every project regardless of Vercel relevance, by using consent prompts.
The rise of AI coding agents has turned software implementation into a low-cost commodity, shifting the primary value of a developer from writing code to defining and verifying complex systems.
Transitioning to a local-first deployment workflow eliminates the complexity of distributed computing for personal projects by making sure that successful local builds translate directly to production without the need to troubleshoot remote infrastructure failures.
Most founders figure out HR ops the hard way — state filings, scattered contractors, benefits nightmares — after something breaks. Alex Cohen, CEO of Hello Patient, built a _40-person distributed team across 15+ states _without the chaos.
SF's tech social scene is driven by the "terminally online," with connections and events organized through platforms like X and Partiful. Traditional social proof is replaced by mutual online interactions, impacting how relationships and trust are formed. This shift suggests a potential broader trend where online social dynamics shape real-world interactions globally.
For the first time in the modern era, software stocks now trade at a discount to the S&P 500. The iShares Software ETF (IGV) is down over 21% YTD and roughly 30% from its September 2025 peak, wiping out about $2 trillion in market cap. The structural problem: AI is breaking the link between headcount growth and revenue growth. If agents replace seats rather than complement them, the per-seat recurring revenue model that justified premium software valuations could unwind entirely.
Venture capital often favors well-networked founders who start with advantages like connections and financial runway, leaving others at a disadvantage from the start. This disparity isn't about in-room investor bias, but rather the different starting lines shaped by existing networks and resources. Underrepresented founders must proactively build credibility within these ecosystems to secure funding, despite systemic inequities.
Legacy enterprise software won by storing records. The next generation of companies will win by building enough trust with enterprises to work with the workflows where important actions actually happen. Trust converts capability into permission to work in enterprise. Anthropic is not the only company pursuing that position, but it is one of the clearest examples of it.
Most people are failing the basic test of whether their AI use cases are even big enough to matter. This essay splits work into execution (automate it) vs exploration (use AI as a thought partner, not an answer machine) and argues that most people should ignore 90% of new AI tools and focus on a small set of mature ones applied to their hardest problems. Good antidote to the anxiety-driven "you must use every new tool" narrative flooding tech Twitter.
Online startup narratives often distort reality through selective success stories and misleading exit optics that hide weak or zero-return outcomes. Cofounder misalignment, role overlap, and shifting contribution levels create common failure modes that surface as resentment or functional redundancy. Recurring friction from legal threats, employee apathy, and relationship-driven sales increases founder workload and emotional strain.
LIGHTFIELD is the AI-native CRM trusted by 2,500+ startups — and now it's even better with Skills. Describe any workflow in plain English — call prep, deal scoring, account research — and trigger it with one sentence. Use the pre-built Skills library [11] to setup
The market can stay irrational longer than most founders can stay solvent. Improving your financial profile should be your top priority. It could take a couple of years. Either get profitable, or ensure that you have enough funds to last for a very long time.
Over 70% of mid-market B2B distribution businesses are expected to change hands in the next decade. This monthly update from the CEO of a B2B logistics software company explores how making operations more systemized and "Amazon-good" can double a business's value before exit. He also makes the case for "one to many" distribution through channels and partnerships over traditional direct sales, arguing the old SaaS playbook is losing steam.
AI will not fix broken product orgs. It will accelerate whatever dynamics already exist. If your team treats engineering as a bottleneck and optimizes around feeding it work, AI just speeds up that dysfunction. If your team is oriented around learning and shaping demand toward real outcomes, AI makes that more effective. Most orgs never examine their "demand mix," meaning where work actually comes from and how it gets shaped, and that gap is about to get a lot more expensive.
Ramp's employees were stuck using AI because of their environment setup, so the company built its own AI productivity suite to make every employee an AI power-user.
Ever wonder how some marketing teams seem to launch one attention-grabbing campaign after another with flawless execution? Asana is the platform that empowers marketers to be 22% more
UGC creators are replacing influencers and traditional production because brands need fast, authentic content that drives measurable results. The creator ad market grew from $13.9 billion in 2021 and could reach $37 billion by 2025. Brands rely on creators to produce high volumes of content quickly and to adapt it for testing and paid media. Consumers trust realistic product demos and relatable experiences more than polished ads, which increases engagement and conversions. This model reduces production time, lowers testing costs, and helps brands scale campaigns more efficiently.
A new usability study shows that AI Mode replaces active purchase comparison with passive acceptance, as 74% of final shortlists came directly from AI output and 88% of users adopted those lists without changes or verification. Users rarely click or research: 64% made decisions without leaving the AI interface, and only 23% visited external sites. Most external visits focused on confirming details instead of finding new options. 74% of users selected the top-ranked AI result, and the average chosen rank was 1.35. Trust depends on AI wording and brand recognition, which drove 37% and 34% of decisions.
Advertisers are likely to keep spending on major social platforms despite legal setbacks because growth has continued through repeated scandals. Meta and Google still posted strong revenue gains after controversies. Ad spend remains steady because these platforms still offer high reach and relatively low costs. This could change if more lawsuits succeed, since a few additional legal wins may shift advertiser sentiment. Rising ad costs and new restrictions on young users may also reduce appeal and push brands toward alternatives like Reddit and retail media networks.
Pages can be highly relevant but still lose rankings if the entity mix does not match search intent. The example shows top results focused more on locations and jurisdictions, while the client page focused on legal concepts and injury types. This gap shows that Google prioritizes geographic and jurisdictional context for local queries. Entity relationships matter more than just having many entities. Content should match the structure of top results by aligning entity types and context to what users expect. To achieve this, analyze top ranking pages, group their entities by type, and update your content to reflect the same dominant relationships.
Most ecommerce brands let their paid media team own the product feed, but organic search and AI surfaces reward different optimization than bid-driven campaigns. A test with a large ecommerce brand found that a dedicated organic feed drove a 92% increase in free listing revenue, 83% more visibility, and a CTR 55% higher than paid for the same period. Google's upcoming Universal Commerce Protocol and new conversational attributes like FAQs and use cases will further tie feed data to how AI agents match products to natural-language queries.
Mutiny shut down its SaaS product and rebuilt as an agentic AI platform for GTM teams. The new product acts as an autonomous agent that creates customer-facing assets like campaigns, decks, and case studies in minutes. It uses brand guidelines, CRM data, and prospect context to generate personalized, on-brand materials and tracks engagement to support sales. The shift removes execution bottlenecks and gives teams faster, more independent output.
Poke is a messaging-first AI agent for iMessage, SMS, and Telegram that helps users get things done quickly. It handles scheduling, reminders, health tracking, smart home control, and workflows through integrations with tools like Gmail and Google Calendar. Poke is proactive, sending alerts and automating recurring tasks, and uses different AI models depending on the job. Its “recipes” let users access or create prebuilt automations and share them with others. The focus is simplicity and accessibility, making powerful agentic AI usable without complex setups.
Content ROI is unclear because it isn't tied to specific business tasks. Structural Content links content directly to a company's jobs-to-be-done by centralizing data, then using agentic AI to scan for gaps where content can help. It generates content job tickets with defined goals, which teams measure against the exact outcome. AI can also run production, campaigns, and iteration by optimizing performance against targets. This replaces broad goals with precise use cases and makes content a direct driver of business outcomes.
Temu built a major pricing advantage by using the de minimis rule to avoid import duties on packages under $800. It shipped individual orders from China at $2 to $4 per package instead of paying 15% to 25% in duties and $15 to $20 in logistics costs. Volume scaled from about 250k daily shipments in 2022 to about 600k in 2023, which made up over 30% of US de minimis imports. It anticipated rule changes and shifted to US warehouses in 2025, which reduced delivery times to 3 to 7 days while absorbing duty costs. The company drove growth through gamification features that led users to make unplanned purchases and supported low prices through subsidies and high marketing spend.
Builder PMs run problems, not updates, by engaging leadership around a clear question and decisions. They drive convergence by separating messy exploration from focused executive narratives.
Commercial products must win in the market, not just solve problems, making them far harder than internal tools. Success depends on strong product discovery to outperform alternatives and drive real outcomes.
The New York Times builds a daily habit by bundling journalism with lifestyle products and maintaining direct user relationships. In an AI world, it wins through human expertise, trusted brand, and destination experiences.
AI enables instant value but weakens retention when users stay passive. The best products turn early value into habits through action, return triggers, and accumulated context.
Agent-first products treat AI agents as a core interface, giving them full capabilities, clean abstractions, and the right context to act independently. Strong systems combine agent autonomy with human judgment through thoughtful design, skills, and continuous iteration.
AI-native teams use a shared “Team OS” repository so agents can access structured context and work autonomously. Success depends on clean context, deliberate planning, and continuous team contribution.
Teams are shaped by their demand mix and how they manage it, not just their processes. AI amplifies these dynamics, accelerating either chaos or learning depending on how demand is shaped.
AI is collapsing PM, design, and engineering into one blended role. What remains are durable advantages: judgment, systems thinking, and organizational power.