Developer Tools1 month ago
SuperCompress - Cut Your LLM Token Costs by 65%
Compress prompts before LLM API calls to reduce token usage and costs.
@asgujjuasitgets · X
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Compress prompts before LLM API calls to reduce token usage and costs.
@asgujjuasitgets · X
Send your question to a panel of LLMs that peer-review each other and return one synthesized answer.
u/Puzzleheaded-Log-27 · Reddit
Building a multi-model AI deliberation tool taught me something about trust LLM Counsel isn't another wrapper around one model - it sends your question to a panel of frontier LLMs, has them peer-review each other anonymously, and an impartial "chairman" model returns one synthesized answer. Free to start, pay-as-you-go after, credits don't expire. What I've learned so far: people trust a synthesized answer a lot more once they can see that the models actually disagreed and how that disagree
Inspect RAG chunks and visualize AI agent workflows, memory architecture, and execution traces.
@Higgs0110 · X
Analytics dashboard for LLM API spending by model and environment with optimization suggestions.
ATsimbalistov · HN
Show HN: Tracking GenAI cost and endpoint fragility so app teams don't have to
Create a personal context file that AI tools read before responding, portable across platforms.
@connorhpbrn · X
the garden for your context
Automatically route each prompt to the cheapest capable model to cut API costs.
u/ASDKING100 · Reddit
Launched an AI API router tonight, and found a bug hours in that would've taken real payments without ever upgrading the account Built LLMLite over the past few weeks — it classifies each prompt and routes it to the cheapest model that can actually handle it, instead of hitting GPT-4o for everything. Free tier, no card needed to try it. Tonight, right as I was about to launch, ran a real transaction to test the payment flow end to end. Paddle processed it, webhook fired, signature verified
API providing token-level citations for LLM output grounded in attention analysis.
apoorvumang · HN
TokenPath – token-level citations for LLM output, read from attention
Semantic caching reduces LLM token costs and latency for AI queries.
u/ornymo_official · Reddit
how to reduce ai costs there are lots of way to reduce costs but there all complex to setup i know this cause i tried one in production so i built ornymo we cache meaning not the exact string allowing us to give same awnsers thus reducing llm costs and latency check it out at ornymo.com free for a limited time and let me know your feedback submitted by /u/ornymo_official to r/buildinpublic [link] [comments]
View LLM model rankings across 10 benchmark questions.
fristovic · HN
She watched me look at model rankings and asked what do the numbers mean... I literally had no good way of explaining it to her so I just came up with something that is approximately in the same ballpark as some of the benchmarks out there lol
Store shared memories once and reuse them across AI systems and your team.
Repeater22746 · HN
ContextVault – Shared memory layer for your AI and your team
Tests LLM endpoints with adversarial cases and provides OWASP-mapped security audit reports.
@aryaan_sheth · X
- LLM security for small teams
Route your LLM API requests across multiple providers to cut costs and meet latency targets.
Aperswal · HN
Made a Free LLM Router