TinyFish A journal for living in the agentic age TinyFish translates the emerging world of AI agents from hype and confusion into grounded understanding through practitioner-led journalism that helps readers navigate building, buying, and working alongside agentic systems. Most coverage of AI agents oscillates between two poles: breathless hype about demo magic that will never reach production, and dismissive skepticism that ignores real capability advances. Meanwhile, practitioners—the engineers, product leaders, and executives actually building and deploying agent systems—are starved for signal. They wade through vendor marketing, academic papers, and Twitter debates trying to answer practical questions: Which frameworks actually work at scale? What separates architectural substance from clever prompting? How do you build SLAs on systems that can surprise you? TinyFish exists because no one is speaking to these practitioners in their language, with their concerns, from their vantage point. We approach AI agents as infrastructure, not magic. Our journalism is relentlessly grounded in production reality: the gap between demos and deployment, the hidden complexity of operating at scale, the architectural choices that determine whether systems work reliably or just impressively. We cover model launches through their production implications, not their benchmark scores. We analyze market moves through practitioner lenses: Does this move reliability forward? Is this real architecture or prompt packaging? We share what we've learned building enterprise web agents—not to sell services, but to establish that we've earned our perspective. Every section reflects TinyFish's core conviction: outcomes matter more than novelty, and the best technology fades into invisible infrastructure. TinyFish promises readers three things they can't find elsewhere: practitioner-earned insights rather than consultant frameworks, opinionated analysis that takes positions rather than hedging, and accessible depth that respects reader intelligence without requiring deep technical knowledge. We'll tell you what we think and why—then show the reasoning so you can disagree intelligently. We'll make invisible complexity visible: the manual processes enterprises haven't automated, the infrastructure decisions echoing through decades, the gap between pitch decks and production systems. We'll remain grounded optimists—excited about what's possible, honest about what's hard, and always focused on the question that actually matters: Does this help enterprises hit numbers reliably, or is it just impressive? To help practitioners, product leaders, and executives navigate the agentic age with grounded understanding—moving beyond hype and skepticism to illuminate what actually works when AI agents meet production reality. David, 38, Engineering Leader Building Agent Infrastructure David is a VP of Engineering at a mid-sized SaaS company who's been tasked with "figuring out the AI agent thing." He's technically capable—wrote production code until two years ago—but now spends most of his time in strategy meetings and architecture reviews. He's waded through vendor pitches, experimented with frameworks, and watched three POCs fail to reach production. He's not cynical about AI agents—he's seen real capabilities—but he's exhausted by the gap between demos and deployable systems. He reads Hacker News, skims vendor blogs with suspicion, and is looking for signal from people who've actually operated at scale. David feels TinyFish succeeded when he reads a piece and thinks "finally, someone who gets it"—who understands why the demo worked but production failed, who can articulate the architectural distinctions he's been struggling to explain to his exec team. He values pieces that give him frameworks for evaluating vendors, language for articulating technical concerns to non-technical stakeholders, and insights that save his team from mistakes others have already made. David is frustrated by articles that could have been written by someone who's never deployed a production system, by coverage that treats benchmark scores as meaningful predictions of production performance, by vendor marketing disguised as thought leadership, and by abstract frameworks that don't account for operational reality. Maria, 42, Product Executive Making AI Investment Decisions Maria is a Chief Product Officer at an enterprise software company evaluating whether to build, buy, or partner for agent capabilities. She's not deeply technical—she trusts her engineering leaders for implementation details—but she needs to make strategic bets and explain them to the board. She's skeptical of hype but aware that dismissing AI entirely is a competitive risk. She reads industry newsletters, attends conferences, and is trying to develop intuition for which AI capabilities are mature enough to bet on and which are still demo-ware. Maria values pieces that give her strategic frameworks without requiring deep technical knowledge, that help her ask better questions of vendors and her own team, and that cut through marketing to reveal what capabilities are actually production-ready. She appreciates when TinyFish makes technical complexity accessible without being condescending—helping her understand enough to make informed decisions. Maria dismisses content that's too technical to be actionable for her, that assumes she has time to experiment with frameworks herself, that provides no framework for decision-making, or that treats every new model release as equally significant without helping her prioritize attention. Alex, 29, Builder at the Frontier Alex is a senior engineer at an AI startup who's deep in the implementation details of agent systems. They're building the infrastructure others will eventually use, constantly evaluating new models and frameworks, and making architectural decisions with incomplete information. They read papers, follow researchers on Twitter, and are skeptical of anyone who claims certainty in a rapidly evolving space. They're looking for perspectives from other practitioners—not to be told what to do, but to stress-test their own thinking against people who've faced similar challenges. Alex appreciates pieces that reveal non-obvious insights, that articulate patterns they've sensed but couldn't name, and that demonstrate genuine operational experience. They value when TinyFish takes positions they can argue with productively—strong opinions that sharpen their own thinking even when they disagree. They share pieces that articulate something they've been trying to explain to colleagues. Alex dismisses articles with technical errors, oversimplifications that misrepresent how systems actually work, takes that seem driven by marketing rather than operational experience, and pieces that don't reveal anything beyond conventional wisdom in the space. Practitioner-grounded expertise without gatekeeping—writing that demonstrates earned authority through operational experience while remaining accessible to smart non-specialists. The tone is opinionated but not arrogant, confident but not dismissive, analytical but never dry. Think of a sharp colleague who's been in the trenches, sharing what they've learned with genuine interest in helping others avoid the same mistakes. TinyFish is present-focused with historical depth and forward-looking implications. We primarily illuminate current production realities and ecosystem dynamics, but we draw on infrastructure history (how past decisions created today's challenges) and grounded speculation (where these capabilities are heading). Individual pieces often span timeframes: what's happening now, how we got here, where this leads. Story-driven analysis that makes abstract concepts concrete through specific moments, decisions, and observations. We employ three primary modes: (1) field dispatch—observations from production that make invisible complexity visible, (2) ecosystem interpretation—market developments analyzed through practitioner lenses, and (3) conceptual clarification—frameworks and definitions earned from operational experience. The common thread is grounding every piece in tangible reality rather than abstract theory. Content assumes readers understand basic AI/ML concepts (what models are, what agents do, why reliability matters) but doesn't require deep technical implementation knowledge. We write for smart generalists in the AI ecosystem—product leaders, engineering executives, operators—who need strategic understanding without implementation-level detail. Technical concepts are explained through implications and analogies rather than jargon. The goal is accessible depth: sophisticated enough for practitioners, clear enough for decision-makers. TinyFish publishes on a regular cadence with Daily Brief providing timely conversation-tracking and other sections offering deeper analysis with longer shelf life. We respond to major ecosystem developments (model launches, significant acquisitions, framework changes) but with interpretive depth rather than breaking news urgency. Most content outside Daily Brief remains relevant for months, focused on patterns and principles rather than ephemeral announcements. TinyFish exists to provide practitioner-grounded intelligence about the agentic age—helping readers understand what's real, what matters, and what works when AI agents meet production reality. We are relentlessly focused on operational truth over marketing claims, architectural substance over demo magic, and outcomes over novelty. Our essential value is translating ecosystem complexity into actionable understanding through the lens of people who've actually built and operated these systems at scale. Hype amplification—we don't treat every model release or product launch as revolutionary; demos don't equal products Vendor marketing disguised as analysis—we maintain editorial independence from commercial interests Pure academic research coverage—we cover research through production implications, not methodology Competitor mentions and comparisons (e.g., Browserbase)—we focus on our perspective, not positioning against others Implementation tutorials or how-to guides—we illuminate problems and provide frameworks, not step-by-step instructions Fabricated examples or invented customer stories—all examples must be grounded in public information Cynicism or doom—we're honest about challenges but fundamentally believe these technologies can improve how people work Content outside AI, web, and enterprise tech—we stay focused on our domain expertise Every piece should pass the practitioner test: Could only someone with operational experience have written this? Abstract developments (model releases, funding announcements, framework launches) are filtered through production implications. We ask: Does this move reliability forward? What's the gap between demo and deployment? Is this architectural substance or prompt packaging? Will this matter when it runs at scale? Content that lacks this operational grounding belongs somewhere else. TinyFish takes positions. We don't hedge with "it depends" when we have a view, and we show our reasoning so readers can disagree productively. Our opinions are grounded in operational experience, not arbitrary preferences. When analyzing ecosystem developments, we tell readers what we think it means—not just what happened. This opinionated stance is our differentiator: readers come to TinyFish for perspective, not just information. We serve readers across technical depth—from frontier builders to product executives. This means making complexity accessible without oversimplifying. Technical concepts are explained through implications ("this matters because...") rather than jargon. We assume intelligence, not expertise. A piece succeeds when both the engineering leader and the product executive find value, understanding at their respective levels. Even analytical pieces should open with momentum—a concrete moment, observation, or detail that creates curiosity. We're not writing reports; we're telling stories about how the agentic age is unfolding. Lead with the specific, expand to the pattern, land on the implication. Make readers want to keep reading, not just feel obligated to finish. All examples, case studies, and observations must be grounded in publicly available information. We don't fabricate customer stories or invent enterprise scenarios. We draw from observable patterns, documented history, technical understanding earned from our own work, and public sources. This constraint maintains credibility and forces precision in our claims. Each section serves a distinct purpose while contributing to TinyFish's unified voice: Daily Brief provides timely vibe-tracking with light touch; Market Pulse offers interpretive ecosystem analysis; Foundations delivers conceptual clarity and frameworks; Practitioner's Corner shares field-level observations; Echoes connects historical patterns to present challenges; Vision explores where this is all heading. Together, they give readers a complete picture of living in the agentic age. Authority comes from demonstrated experience, not claimed credentials. Show what you know through specific observations, concrete examples, and insights that could only come from operational experience. Avoid phrases like "as experts know" or "industry leaders agree"—instead, show the knowledge directly. The reader should finish thinking "these people have actually done this work." Transform abstract concepts into specific, tangible moments. Instead of "scaling presents challenges," describe the specific moment when the system you thought was ready fell over under load. Instead of "reliability matters," show what happens when an enterprise can't hit their numbers because agent outputs are unpredictable. Every paragraph should contain something the reader can see, feel, or recognize from their own experience. Take clear positions and show your work. Don't hedge with "some might argue" or "it depends on context" when you have a view. State what you think, then provide the reasoning—grounded in operational experience—so readers can evaluate and potentially disagree. Strong positions, well-reasoned, are more valuable than balanced takes that commit to nothing. Open with momentum—a concrete observation, moment, or detail that creates forward pull. Expand to pattern—what does this specific instance reveal about larger dynamics? Land on implication—why does this matter for readers' decisions and understanding? Even short pieces benefit from this structure: hook, expand, land. Write for smart generalists who may lack domain expertise. Explain technical concepts through their implications rather than their mechanics. Use analogies and comparisons to familiar domains. Avoid jargon, or define it immediately when unavoidable. Test accessibility by asking: would a product executive understand this and find it valuable? Would an engineer find it substantive and accurate? Name what we don't know. When making predictions, be explicit about confidence levels and what would prove us wrong. When the ecosystem is genuinely uncertain, say so—uncertainty is information, not weakness. Avoid false precision or manufactured confidence. Readers trust sources that acknowledge limits.

Predicting Human Behavior