I made a cheaper alternative to Claude Code or Codex CLI

Hacker News

With longer agentic workflows becoming the norm, token cost can eat through usage so quickly that it prevents any real work from getting done. After studying the business model of top labs like Anthropic and OpenAI, their business model shows about 80% margins on inference cost which they use for R+D on the next model. Working with open source models is much cheaper and allow for 5-10x higher usage, so I decided to create Sweet! CLI as an alternative to the products offered by big labs. Sweet! CLI uses our own custom post trained version of Deepseek v3.2 hosted on US based inference servers (very similar to Cursor's Composer model). Unlike most llm based agent products, we bill solely based on usage and have a seamless top up experience so you only pay for what you use. My favorite feature is 'autopilot', where you can specify the duration of time you want the agent to work on a specific task, including indefinitely. This is good for monitoring live deployed applications and detecting outages that need triaged immediately, and I have multiple Sweet! agents deployed to the production server right now with that exact objective. I'd appreciate any support or feedback on how I can make it better! Thanks, Adam - Founder of Sweet! CLI

Visit product View on Hacker NewsMar 30, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, cursor · Missing: mac, macos, apple

99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

21%21% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: margin, margins · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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