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Build or Skip (buildorskip.dev) is a product opportunity research platform for independent developers, startup teams, and website owners who want evidence before they commit to a build. It aggregates public payment signals, voluntary tip revenue, domain intelligence, and backlink resources into filterable rankings, then lets you compare momentum across months so you can judge whether a category is still rising or already cooling. The homepage states the mission in one line: stop guessing what to build. Everything else on the site, from the leaderboards to the site profiles to the API and MCP integration, exists to support that single decision. Website Positioning Build or Skip sits between idea discovery tools that only show what is trending and full competitive intelligence suites that cost more than most indie projects earn in their first year. It is narrower than a market research platform and considerably more evidence-driven than a launch directory. The positioning is decision support before code. The homepage frames the workflow in three steps: find opportunities, read the trend, make the call. It also names the three verdicts the product is designed to produce, which are Build, Watch, or Skip. That structure is deliberate. The platform is not selling ideas; it is shortening the distance between an observed signal and a committed decision, which is exactly where solo builders lose the most time. A second positioning choice is candor about data limits. Registration dates do not establish launch dates. Payment signals are traffic estimates, not verified revenue or orders. Tip amounts are publicly visible amounts, not confirmed income. This restraint makes the published signals more useful rather than less, because it forces you to treat them as evidence instead of truth. Target Audience The primary audience is the solo builder or two-person team shipping small web products, usually a SaaS tool, a directory, a marketplace, or a niche content site. These users rarely have budget for enterprise research tools and rarely have months to spend on discovery. They need a fast answer to a narrow question: is anyone paying for something like this right now? The second audience is the small startup team doing quarterly roadmap planning. A founder and a product lead can use the product revenue ranking to check whether a planned feature overlaps with a category that has visible momentum, and whether the leaders in that category are growing steadily or simply trading rank month to month. Growth marketers and SEO consultants form the third group. They come for the comment backlink library and the AI tool backlink library, which list submission URLs, submission methods, fee requirements, and last-check dates. That turns a tedious prospecting chore into a sortable list. Early-stage investors, scouts, and analysts make up the fourth group. They use new-site rankings to spot freshly registered domains that already generate payment signals, then cross-check domain rating, registration date, product category, and capability tags to decide whether a deal deserves a conversation. Product managers and analysts inside small SaaS companies are a fifth audience. They use the six-month momentum columns to sanity-check competitive assumptions and to see which adjacent categories are heating up. A sixth group is the creator economy: newsletter writers, community moderators, and directory operators who need credible, source-linked data for posts and weekly digests. Finally, agencies and freelance consultants use the platform as a scouting layer. A ready to use ranking that refreshes every month gives them a defensible starting point when a client asks where the next opportunity is. Core Features The centerpiece is the new site revenue ranking. It lists newly registered domains that show payment signals, sorted by rank, with domain rating, domain creation date, payment channels, primary and secondary category, product type, and capability tags. A published preview for a recent month showed 185 sites, with the leader carrying more than fourteen thousand payment signals. The homepage also exposes headline counters such as 50k+ ranked revenue sites, 4,900+ tip entries, and 500+ backlink entries, so you can gauge dataset size before you subscribe. The product revenue ranking is the working horse. You can filter by product area, payment channel, domain rating, registration date, and payment signal volume, then compare six months of movement side by side. This is where a spike gets separated from a trend. The tip revenue ranking narrows in on publicly visible Buy Me a Coffee amounts. It tracks active months and six-month amount trends, which is a surprisingly strong demand signal: people rarely tip for something they do not value. Two backlink libraries round out the research layer. The comment backlink library organizes collected comment articles by domain and review date, with direct links to the original page so you can judge quality yourself. The AI tool backlink library catalogs AI product directories and resource sites with submission URL, submission method, fee requirement, and last check date, which makes outreach more efficient and far less guesswork. Monitoring features include a sitemap monitor for up to twenty public sitemaps on the monthly plan and fifty on the yearly plan, plus a keyword monitor, a watchlist for tracking candidates over time, and a Chrome extension for capturing sites while you browse. For programmatic users, an API returns domain rating, registration date, monthly traffic, and structured homepage data. Paid tiers receive unlimited queries, and MCP access lets supported AI clients browse the dataset catalog, discover products, and read site snapshots with unlimited calls. Content Features The content layer is written for practitioners rather than analysts. The homepage is short and direct, with plain-language explanations of what each signal means and what it does not. The rankings print their caveats next to the data instead of burying them in a footnote, which is unusual and welcome. A built-in FAQ answers the questions buyers actually ask: who the tool is for, what tipping behavior reveals about demand, how to use the two backlink libraries, and how to request a correction or removal if your own site is listed. There are also practical tips and tricks threaded through the interface in the form of filter hints, category labels, and coverage toggles that make the dataset easier to navigate without a tutorial. Short dated stamps appear on rankings and site records, so freshness is always visible. User Experience The experience is utilitarian and fast. Tables load quickly, columns are sortable, and every metric carries a short definition or a footnote. The site supports a Chinese locale in addition to English, and the language switcher sits in the top navigation next to the sign-in link. Navigation groups the product into revenue ranking, tip ranking, backlink leaderboard, sitemap monitor, keyword monitor, Chrome extension, API, MCP, watchlist, and pricing, so the information architecture mirrors the workflow rather than a marketing funnel. A ranking preview section sits above the pricing block, letting visitors see the shape of the data before they commit. Feedback is presented as privacy-redacted screenshots translated into English, which is an honest, low-polish approach that matches the tone of the rest of the product. Technical Features Revenue rankings follow calendar-month reporting, while comment and AI tool backlink resources update daily, and other site data displays its relevant date directly on the page. The API exposes domain rating, registration, monthly traffic, and structured homepage data through a single account token. MCP support means supported AI clients can query the catalog and pull site snapshots directly into a conversation, which removes a manual copy-paste step from the research loop. None of these technical pieces are decorative; they exist to make a repeated research habit more efficient. Pricing and Access Model Build or Skip runs a freemium model. The free preview shows the shape of the rankings, and Pro unlocks complete rankings, all filtering and sorting dimensions, the full comment and AI tool backlink libraries, sitemap monitoring, MCP access with unlimited calls, and a VIP community. Pro Monthly costs 19 dollars per month. Pro Yearly costs 190 dollars billed yearly, which works out to 15.83 dollars per month and gives two months free, plus API access with unlimited queries and a higher sitemap monitoring limit. The site warns that standard Pro pricing will rise after the next feature release, and that partner pricing renews at the same rate as long as the subscription stays continuous. Why It Matters Most failed products are not badly built, they are built in the wrong place. Build or Skip does not remove risk, and it does not claim to. What it does is replace a guess with a documented signal, a trend line, and a timestamp, so the decision to build, watch, or skip is at least anchored to something observable. For a solo developer deciding where to spend the next ninety days, that is the difference between a cheap experiment and an expensive lesson.

Overview MiroFish is a chat-first prediction engine that turns a plain-language question into a living scenario. Instead of returning one confident answer, it builds a knowledge graph, runs a multi-agent simulation, and hands back a structured prediction report. You ask the question the way you would ask ChatGPT, and the system handles seed material, simulation, and reporting as one continuous workflow. The product states its own promise plainly: predict anything, but talk to it like ChatGPT. That single sentence captures both the ambition and the interface. The ambition is broad scenario prediction for questions where human reaction drives the outcome. The interface is deliberately familiar, so the learning curve stays close to zero. Website Positioning MiroFish does not position itself as an oracle. It positions itself as a rehearsal space. The homepage makes the distinction explicit in its comparison section, which sets a single chat answer, manual research, and the MiroFish simulation side by side. A single chat answer is fast but collapses competing audience reactions into one confident response. Manual research is grounded and careful but slow when a decision depends on many groups influencing each other at once. MiroFish sits between them: a ready to use environment where agents, memory, social surfaces, emergent clusters, and a report you can keep questioning live in one place. The value proposition is not absolute accuracy. It is speed of insight into how reactions might compound. Why Reaction Beats a Static Answer Most planning questions fail not because the analysis is wrong, but because the analysis ignores feedback loops. A price change does not produce a single customer response. It produces a first reaction, a narrative that forms around that reaction, and a second wave of people reacting to the narrative rather than the price. A policy announcement behaves the same way. MiroFish is built for this class of problem. Its use case sections frame the product around moments where reaction matters more than a static answer: campaign tests, pricing reactions, policy stress tests, and market narratives. Each moment has the same shape. A small group moves first, a story forms, and everyone else responds to the story. Target Audience The tool targets anyone whose decision depends on how groups of people will respond to each other. Product marketers use it to pressure-test a launch narrative before the first dollar of spend. Pricing and revenue managers use it to model which customer segment pushes back first after an increase. Policy and public affairs teams use it as a tabletop exercise for coalition formation and second-order reactions. Strategy consultants use it to generate hypotheses faster than a research cycle allows. Founders use it to sanity-check positioning against a skeptical category. Crisis communications teams use it to rehearse how an accusation could outrun an official explanation. Screenwriters and narrative designers use it for creative continuation. The common thread is that all of these users need plausible reactions, not a single verdict. Core Features The product is text-first by design. You start with a question, then decide whether supporting files are necessary, without losing the speed of a chat interface. An orchestration layer runs graph building, simulation, and reporting behind the scenes while you stay inside one conversation. Every answer ends with a structured result card that contains a summary, a report entry point, and a follow-up path. Optional attachments in PDF, Markdown, and plain text act as reality seeds, grounding the simulation in a specific strategy memo, product FAQ, policy brief, market note, or research summary. A sample orchestration preview on the homepage shows three example questions so a first-time visitor can see the shape of a good prompt before committing to one. Knowledge Graph Step Before any agent speaks, the system extracts actors, relationships, pressures, and factual anchors from your question and any attached material. This step is why the output holds together. A simulation that starts from raw text tends to drift, because the model has no explicit structure to reason from. Building the graph first forces the scenario to name its stakeholders and the forces acting on them. In practice, this is often the most valuable part of the run, because writing the actors and incentives down reveals assumptions you did not know you were making. Users who want tighter results learn to front-load this step by naming the decision, the audience, the trigger, and the time horizon in the question itself. Agent Simulation Step The simulation stage lets personas interact across short-form and threaded social surfaces over multiple rounds. Agents carry memory, so a position taken in round one shapes behavior in round three. Clusters form. Moderate voices stay quiet until a specific framing appears. Early backlash from one segment supplies vocabulary that later segments reuse. This is the part that separates the product from a sentiment dashboard, because sentiment dashboards tell you what people think now, while the simulation explores what they might think after hearing what everyone else thinks. The number of rounds, the surfaces, and the persona set remain adjustable through how you frame the question and what seed material you provide. Prediction Report The run condenses emergent behavior into a structured report. A typical report contains an executive summary, risk signals, narrative paths, and follow-up questions. The sample report previewed on the site covers a price increase and identifies the highest-risk path as a compressed story that turns the announcement into a trust issue before value evidence becomes visible. Risk signals list early backlash from price-sensitive segments, narrative compression into a simpler accusation, and influencer framing that outruns the official message. Narrative paths describe what happens if the value story holds, if comparison charts are missing, and if supporters lack reusable language. That structure is designed to make the next question obvious rather than to close the topic. Deep Interaction The workflow does not stop at the report. You continue asking questions against the generated world. Which persona creates the first negative cascade. What changes if we announce a transition plan. Which evidence line reduces confusion fastest. This is where the most efficient use of the tool appears, because a single seeded world can answer a dozen related questions that would each require a separate research project in the physical world. Follow-up questions also function as an assumption audit, since the answers show which variables actually move the outcome and which ones barely matter. Prompt Craft and Playbooks The site ships short, practical playbooks so a visitor gets value before opening the full product. The guidance is direct: name the decision, the audience, the likely trigger, and the time horizon. A narrow question gives the simulated world less room to drift. The playbook section also covers seed files and report reading, and it collects the tips and tricks that experienced users converge on, such as treating the output as decision support and hunting for resistance signals, narrative bridges, and assumptions worth checking with real data. The recommended next step after reading a report is to ask which group changes the result if its incentive changes. Use Cases in Practice Four scenarios anchor the product page. Campaign test asks what happens if a positioning statement launches in a skeptical category, and it simulates how audience groups might amplify, resist, or reinterpret the message before money is spent. Pricing reaction explores the friction behind an increase by modeling sentiment, value perception, and objection paths across segments before the announcement. Policy stress test acts as a tabletop exercise for controversy, coalition formation, and second-order effects. Market narrative examines how narrative, incentives, and sentiment interact in situations where spreadsheets miss the feedback loop between analysts, retail attention, and public discourse. Each scenario shares a common purpose: surfacing the reaction you did not budget for. Content Features The homepage is unusually task-oriented for a landing page. It pairs bilingual section labels in English and Chinese with concrete sample prompts, a workflow diagram in five steps, four playbooks, a comparison table, a report preview, and a plain FAQ. The comparison table does real work, because it tells a visitor when not to use the product as clearly as when to use it. The FAQ avoids overclaiming and states that the output is exploratory decision support rather than a guaranteed forecast. A long row of small directory badges sits at the bottom of the page, kept deliberately compact so the product experience stays focused. User Experience The experience is built around staying inside a single conversation. There is no separate graph editor to learn, no dashboard to configure, and no report builder to assemble. You type a question, attach files only if they help, and read a result card that links to the deeper report. The text-first approach makes the first run fast and the hundredth run faster. Because the orchestration runs behind the chat, the user never has to manage a pipeline manually. That design choice is what makes the tool practical for someone who needs an answer before a meeting rather than after a research sprint. Technical Features Under the interface, four technical components cooperate. A knowledge graph layer extracts structure from unstructured input. A multi-agent simulation layer runs personas with memory across multiple rounds and multiple social surfaces. A synthesis layer compresses emergent behavior into narrative paths, risk signals, and confidence cues. An orchestration layer sequences the three and keeps the conversation state intact across follow-up questions. File ingestion handles PDF, Markdown, and text, which covers the seed formats that knowledge workers actually produce. The architecture is designed for repeated interrogation of a persistent world rather than one-shot generation. Limitations and Honest Framing The product repeats one caution in several places, and the repetition is a feature. MiroFish is not a guaranteed forecast. Personas are plausible archetypes rather than your actual customers. Reports are designed to be edited by a human before they reach an executive audience. Any result worth acting on deserves validation with analytics, customer research, or a real pilot. Users who treat the output as a hypothesis generator get the most from it. Users who treat it as a verdict will be disappointed. The site says as much, which is rare and useful in a category full of confident claims. Getting Started The fastest path is to open the chat and type one narrow question about a decision you already face. Add a seed file only when the scenario needs specific actors, incentives, or prior context. Read the result card, open the report, then ask the follow-up questions the report suggests. Keep the question tight, keep the time horizon explicit, and treat every run as a rehearsal. When the question is worth simulating, MiroFish turns a few minutes of typing into a structured world you can interrogate for hours, which is a more efficient trade than most planning tools offer.

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Bibliou là một nền tảng đọc sách trực tuyến cho phép người dùng khám phá và đọc các cuốn sách mới nhất và nổi tiếng nhất. Với Bibliou, bạn có thể dễ dàng tìm kiếm và đọc các cuốn sách từ các tác giả nổi tiếng, cũng như khám phá các từ mới và xu hướng mới trong thế giới sách. Nền tảng này cung cấp một trải nghiệm đọc sách tuyệt vời với các tính năng như đề xuất sách theo tác giả, danh sách các cuốn sách bán chạy nhất và các tính năng tương tác khác. Bibliou hướng tới cộng đồng người đam mê đọc sách, giúp họ dễ dàng tiếp cận với các tác phẩm văn học mới nhất và tốt nhất.

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