Welcome to Homebase AI - A weekly newsletter where we share our interviews with founders & leaders building next-gen AI companies and curate interesting news, insights & trends in the AI space for our community members.

What’s on tap today:

• Our interview with Amogh Mundhekar

• AI Jobs You Can’t Miss

• Headline Recap

• What's trending in AI this week

• Community Update

• How Homebase Helps You

AI FOUNDER STORY

Ship faster with AI: lessons from Bind AI

Quick Background

Amogh Mundhekar, an ex-Intuit/Mailchimp AI product lead, co-founded Bind AI to disrupt software development. Launched in 2023, Bind has already attracted around 14,000 startup builders. It’s an AI-powered platform where tech-savvy founders can input ideas, images or GitHub code and get a full app back, live preview and one-click deployment included.

What if your next app built itself?

Imagine describing your app idea to a chatbot and getting a working product in return. That’s Bind AI’s pitch: founder-friendly development without a coder in sight. Mundhekar calls Bind a “developer-less dev tool”, just chat or upload an image/GitHub link, and Bind writes, previews and deploys your entire application.

How Bind AI Works

Bind is aimed at startup founders or “hackers” who are comfortable with tech but not full-time programmers. Give Bind an idea (or a visual/mockup or your existing code), and it generates a complete full-stack app all within the same environment. The team started with a simple chat-to-code interface, then added live HTML previews, Next.js support and one-click publishing essentially a more streamlined, AI-enhanced version of tools like Vibe Coding’s Windsurf.

Watch the full interview below! 👇

The Ultimate Lesson

Mundhekar’s key takeaway: AI isn’t a passing trend. It’s becoming the backbone of every product. Big tech is already all-in on AI (think Google’s Gemini or Meta’s new AI push), so every app will soon have AI baked in. As he puts it, “AI is not gonna take your job, but someone using AI will.” In short, adapt now or get left behind.

The Evolution of AI Tooling

AI development tools have advanced at warp speed. Just two years ago an “AI agent” was mostly a clever prompt. Today we have true specialized AI assistants. For example, there are now versions of Claude fine-tuned for finance and Tesla preparing a Grok chatbot for cars. The upshot: instead of one generic bot, industries are getting their own GPT-X or AI agent (even voice-driven AI in vehicles).

How AI is Reshaping Development Timelines and Roles

Mundhekar predicts development schedules will shrink dramatically. Tasks that used to take months (building an MVP, testing, bug-fixing) can now happen in days with AI writing and testing the code. As a result, product teams can be much leaner: dedicated QA testers and junior developers become less critical when AI handles debugging and iteration. In practice, a solo or tiny team armed with Bind AI can now deliver an entire product that once required dozens of engineers.

Curious how Amogh is building a developer-less dev tool? Read the Full Interview

Listen on Spotify | Apple Podcasts 
Watch on YouTube

JOIN THE AI REVOLUTION
AI JOBS YOU CAN’T MISS!

Top Picks:
+   OpenAI - Offensive Security Engineer, Agent Security
+   OpenAI - Customer Education Community Manager, Champion Network
+   Hugging Face - Cloud Machine Learning Engineer - EMEA remote
+   Lambda AI - Data Center Business Operations Principal Lead
+   Scale AI - Deep Research Agent Tech Lead
+   helsing - Network Engineer - High Side Engineering
+   Synthesia - Senior Product Designer
+   Anduril - Computer Vision Engineer, Geometry & Perception
+   Figure AI - Staff Data Engineer
+   Anthropic - Brand Marketing Manager, Claude
+   Appier - AI Creative Designer
+   Nice - Director, Legal M&A and Strategic Projects
+   OpenAI - Research Engineer / Scientist, Pretraining Safety
+   X AI - Exceptional Engineer - AI Coding Tools
+   X AI - Sr. Partner Manager, Developer Platform

View all jobs here

HEADLINE ROUNDUP
Headline recap

• Apple Set to Pay Google $1B Yearly for Siri Upgrade

  Apple is on the verge of a deal to pay Google $1 billion annually to boost Siri's capabilities.

  👉Read here

• Snap Shares Surge on $400 Million Perplexity AI Deal

  Snap plans to integrate an AI-powered search engine by investing $400 million in Perplexity, opening new business possibilities for Snapchat.

  👉Read here

• Stability AI's Legal Victory Impacts Copyright Law

  Stability AI wins against Getty Images, raising questions about copyright law. The company is also enhancing AI music with Udio.

  👉Read here

• New Malware Uses AI to Adapt During Attacks, Report Finds

  A report reveals AI-driven malware that adapts its strategies during attacks, creating concerns in cybercrime and technology sectors.

  👉Read here

• Waymo, Alphabet Sued for Bias After AI Allegedly Mislabels SF Doctor as Terrorist

  Waymo and Alphabet are facing a lawsuit for AI bias after a doctor was wrongfully labeled as a terrorist, with the company failing to correct the error.

  👉Read here

TRENDS
What's trending in AI

TF-IDF

• Key Player: A foundational scheme that multiplies term frequency by inverse document frequency to weight word importance across a corpus, with IDF rooted in work by Karen Spärck Jones and influencing modern rankers like BM25

• Market Value: Embedded in mainstream tooling such as scikit‑learn and supported across enterprise search platforms, TF–IDF provides low‑cost, explainable lexical relevance for production pipelines.

• Adoption: Broadly used via libraries (e.g., TfidfVectorizer) and frequently paired with BM25 as a baseline or component in LLM/RAG retrievers.

• Recent Developments: Hybrid search that fuses sparse (TF–IDF/BM25) with dense embeddings is now a recommended pattern to boost precision and recall in RAG and enterprise search.

Growth: Search interest is 49.5K with +99X+ growth, reflecting a sharp spike as teams operationalize hybrid retrieval patterns.

Why It Matters: Combining TF–IDF’s precise keyword matching with embeddings reduces missed terms and improves grounding and overall relevance at low latency and cost.

The Big Picture: Classic IR signals like TF–IDF are converging with neural methods into standard hybrid pipelines that outperform either approach alone.

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