Phind-405B and faster, high quality AI answers for everyone

Original Article ↗ Hacker News Discussion ↗

Usage patterns and strengths

  • Many use Phind as an AI-enhanced technical search engine, especially for programming, APIs, debugging, and infrastructure “how do I do X?” tasks.
  • Several report it as a strong productivity booster, getting them from near-zero knowledge (e.g., AWS VPC/NAT/Fargate) to working solutions quickly.
  • Common workflows: replacing Google + Stack Overflow; summarizing articles via URL; code optimization and debugging; niche language questions; learning new tech concepts.
  • Users like the presence of linked sources when they appear, treating Phind as “search + oracle” rather than pure chat.

Comparisons with competitors

  • Compared with ChatGPT, some prefer Phind for citations and technical focus, and as a fallback when ChatGPT has access/captcha issues.
  • Others prefer Kagi Assistant, Brave Search, Bing + GPT‑4o, Perplexity, or Claude for equal or better answers, broader features, or fewer UI issues.
  • Several note Phind-70B and now 405B can be competitive with Claude/GPT‑4 on some coding tasks, while GPT‑4 remains best for certain formatting tasks.

Hallucinations, accuracy, and verification

  • Multiple reports of confident but wrong answers: nonexistent language features, incorrect C++/Laravel examples, misdescribed hardware, and factual questions without valid references.
  • Users appreciate when Phind later admits a reference error or, in newer runs, detects nonsensical queries and corrects itself.
  • Some say “Always search” sometimes fails to trigger; others see answers improving when rerun.
  • General consensus: model is powerful but must be treated skeptically; follow‑up questions and checking sources remain essential.

Speed vs. quality

  • Thread discusses latency as a key barrier for AI search versus classic search.
  • Some argue that while token-by-token generation is slower, total “time to understanding” can be faster than traditional search, provided answers are accurate.

Product experience and UI

  • Positive: VS Code extension, “artifacts”-style features in development, improved search pollution, and better answer organization promised.
  • Negative: buggy web UI (scroll jumps, input obscured on mobile), occasional inference outages, region blocking (e.g., Malaysia), and some users being IP‑blocked.

Pricing, access, and “for everyone” claim

  • New Phind‑405B is only for paid Pro users; “for everyone” is interpreted by some as misleading marketing.
  • Phind Instant remains free; some want at least a small free quota for 405B to trial it.
  • Pricing criticized for having only a $20/month tier; some want cheaper, low‑usage plans.

API, ecosystem, and openness

  • Many request an API and OpenRouter‑style access so they can integrate Phind into their own tools and compare it on public leaderboards.
  • Company indicates API is lower priority than the main product but is now under consideration.
  • Some want weights released (especially Instant/70B), with debate over whether Llama’s license requires that; unclear from thread.
  • Concerns raised about opaque data handling and trustworthiness; one user says attempts to clarify for corporate use went unanswered.

Model behavior and philosophy

  • Long subthread critiques LLM “apologies” and anthropomorphic phrasing as misleading, since models lack real understanding, memory of wrongdoing, or capacity for genuine care.
  • Others stress that hallucinations and lack of “I don’t know” are structural to current LLMs; research on source‑aware training and better reasoning is referenced as a path forward.
  • Some propose using LLMs mainly to generate good search keywords and filter human-written sources, rather than as direct answer generators.