Phind 2: AI search with visual answers and multi-step reasoning
Phind has launched a new version of its AI-powered search engine that emphasizes developer-focused answers, visual explanations such as auto-generated Mermaid diagrams, and multi-step reasoning. Users generally praise its coding help, UI polish, speed, and source integration, often comparing it favorably to Google and sometimes to Perplexity or ChatGPT, though some find the outputs overly verbose, certain features buggy, and want finer controls, trials, or usage-based pricing. The update also surfaces concerns about copyright and content creator compensation, the need for APIs and IDE integrations, and how AI search can coexist with or eventually supplant traditional search engines.
Overall reception & positioning
- Many commenters are impressed; several paying users say Phind now rivals or beats Perplexity, ChatGPT, and mainstream search for programming and some research/finance tasks.
- Others still find Perplexity or ChatGPT better for certain queries (e.g., product search, playful/creative responses, vague memes).
- Some feel Phind disappeared from view for a while and needs more visible presence to stay top-of-mind.
Visual answers, diagrams & UX
- Strong enthusiasm for on‑the‑fly diagrams (via Mermaid), flowcharts, and image-rich explanations; people highlight them as the standout value vs. competitors.
- Others find diagrams verbose, distracting, or slow to render, especially when they restate a simple question or clutter code answers. They want an easy per-query toggle or a “plain text only” profile that doesn’t require sign-up.
- UI is widely praised: side-panel sources, rich layout, “article-like” responses, and tree-structured conversations with “zoom into a part of the answer” follow‑ups. Some want denser layouts to see more content at once.
Developer & power‑user use cases
- Heavy use for coding help, SQL query construction, complex API setups, architecture diagrams, LangChain examples, and IDE integration.
- Some miss the deprecated VS Code extension; others want APIs and broader IDE support (e.g., IntelliJ, Continue plugin).
- URL ingestion sometimes fails (e.g., resume + job posting), which users expect a “search engine LLM” to handle reliably.
Model behavior, quality & trust
- Reports of excellent, fast answers on technical topics (tax AMT/LTCG, specific stocks, programming).
- Other queries show glaring errors: hallucinated economic calendars, wrong claims about GameStop in Canada, confusion over “should I?” vs. cited sources, and weaker handling of internet memes.
- Several users note models are overly agreeable and not sufficiently grounded in the actual retrieved sources.
Business model, access & privacy
- Pricing criticized for forcing a $20/month subscription just to try premium models; many request usage-based or short paid trials.
- Free tier limits and countdown behavior are described as buggy/confusing.
- Concerns that “new threads are public” by default and that training on user data is opt‑out; some only want anonymous, no-account use.
- Region-based unavailability frustrates some.
Web ecosystem, copyright & infrastructure issues
- Strong debate over Phind displaying third-party images in answers: critics see copyright violation and reduced incentives for creators; suggestions include using only CC-licensed media or revenue-sharing schemes.
- Defenders compare this to Google Images and invoke fair use, but others note prior lawsuits against image search engines.
- A serious bug is reported: shared conversation links behave like mutable sessions—anyone can edit the content and it persists for others—creating confusion and potential security/abuse concerns.