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Waveloom
Terminal coding agent built for DeepSeek's cache economics
Hey PH! I'm Menfre, maker of Waveloom.
I've been using Claude Code since day one — love the UX, hate the token burn.
So I built Waveloom: a terminal-native AI coding agent in Go, with the same
TUI quality, but engineered for cost efficiency at scale.
The key difference is persistent context caching — a 4-level compaction
system (Snip/Prune/Summarize) that keeps cache hits high across long
sessions. No more paying to re-read the same context every turn.
Also built-in: multi-agent orchestration (Fork, Explore, Cold, verification),
native MCP client, plan mode, and a skill system. All in one binary.
Open source, written in Go + Bubble Tea. Try it, break it, tell me what sucks.
Nice focus on cutting DeepSeek costs with prefix caching. One thing that would help me adopt it faster is a built-in token and cost dashboard per session, so I can see in real time how much I'm saving versus a regular call, and track it across projects.
A 1/50 cost reduction with DeepSeek caching sounds huge. One thing that would help adoption though is adding a local token usage dashboard so we can actually see the cache hit ratio in real time and confirm the savings match what's claimed.
The 95% cache hit rate is honestly wild for a terminal agent, love seeing pure Go in this space too. One thing that would make me reach for this daily though - adding a small inline cost tracker that shows estimated tokens saved per session, maybe even a running total in the status bar. Would make the 1/50 cost claim feel tangible instead of something you have to take on faith.
About Waveloom on Product Hunt
“Terminal coding agent built for DeepSeek's cache economics”
Waveloom was submitted on Product Hunt and earned 0 upvotes and 4 comments, placing #70 on the daily leaderboard. 为 DeepSeek 前缀缓存定制的终端 Code Agent（纯 Go），缓存命中率 95-99%，输入成本降至 1/50。A terminal coding agent optimized for DeepSeek prefix caching — 95-99% cache hit, 1/50th the cost. - Menfre01/waveloom
Waveloom was featured in Developer Tools (515.9k followers), Artificial Intelligence (473.7k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 208.1k products, making this a competitive space to launch in.
Who hunted Waveloom?
Waveloom was hunted by Menfre. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Menfre