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Show HN: Prompts are coupled to LLMs and nobody builds tooling for it
I went down a rabbit hole trying to understand why my Claude prompts turn to garbage on GPT-4 and vice versa. Not just "slightly worse" — fundamentally broken. Turns out researchers have already measured this: removing colons from a prompt template swings LLaMA-2-13B accuracy by 78 percentage points (Sclar et al., ICLR 2024). The format that works best on one model family overlaps less than 20% with what works best on another (He et al. 2024).<p>So I went looking for a tool that handles this. Checked DSPy, Guidance, Outlines, PromptLayer, LMQL, Braintrust, Humanloop, Maxim, MLflow, Prompty, Promptomatix. Eleven tools. Zero of them adapt input prompt format per model. They all either optimize what the prompt says or constrain what the model outputs. The actual structural packaging of the input? Manual everywhere.<p>Then I looked at how production tools deal with it today. Aider has a 2,718-line YAML file with 313 model configs. Some models get "you NEVER leave comments without implementing" and Claude gets the literal opposite instruction. Claude Code only works with Anthropic models — third parties have built LiteLLM proxies and Node.js fetch interceptors to hack around it. Cursor's docs say "switch to a different model and try again."<p>The paper maps this to Constantine's coupling taxonomy from 1974 (content, common, control, stamp, data coupling). Same structural problem, different domain. I called it "prompt coupling" because that's what it is — your prompt is coupled to your model the same way a module can be coupled to another module's internals.<p>Also built promptc (<a href="https://github.com/shakecodeslikecray/promptc" rel="nofollow">https://github.com/shakecodeslikecray/promptc</a>) — transparent HTTP proxy, rewrites prompt structure per model, zero code changes to existing tools. It's a proof of concept, not a product. The paper is the actual contribution.<p>First paper. Independent researcher. If the framing is wrong, I'd rather hear it here than after it's indexed.
Anthropic's pricing wall is routing enterprise revenue to OpenAI
I've spent the last week trying to use Claude Opus in an IDE via a localhost proxy that wraps the Claude Code CLI.The proxy exists because there's no way to get programmatic API access to Claude on a Pro ($20/mo) or Max ($200/mo) subscription.This isn't an oversight. Anthropic has actively taken steps to prevent subscription-based API access. They've shut down third-party bridges, tightened terms of service, and made it clear this is corporate policy, not neglect, n
Show HN: Jsonchunk – Parse incomplete JSON from streaming LLM responses
GitHub: https://github.com/jbingen/jsonchunknpm: https://www.npmjs.com/package/jsonchunkIf you're building on top of LLMs with structured output, you've hit this: the model streams JSON token by token, but JSON.parse throws on anything incomplete. So you either wait for the full response (bad UX) or write hacky string recovery logic to show partial results.jsonchunk is a tiny tolerant recursive descent parser that extracts the best-effort object
Show HN: MSAM – Memory system for AI agents that knows when it doesn't know
I was originally deploying OpenClaw and found their method of storing data in the workspace very inefficient and even with attempting a a hierarchical memory in the workspace just- made data stale, incorrect, and unsure when it wrote data exactly. All of this while loading massive chunks of data into my context window.<p>Hence why I ended up with MSAM. It stores data as discrete atoms across four cognitive data streams. (Working, Semantic, Episodic, and Procedural) Retrieval scoring runs on math, not LLM calls — it uses ACT-R activation theory from cognitive science to rank what matters. That cuts costs on both ends: no LLM overhead for search, and compressed output instead of dumping everything into context. It also knows how recently it was accessed, how stable it's proven over time, and how relevant it is to the current query- the same forgetting curve and access patterns cognitive science has measured in human memory since Ebbinghaus. On top of that, a knowledge graph of subject-predicate-object triples tracks structured facts with temporal validity, so the system knows not just what was true but when.<p>Most memory systems ask for a static amount of memories on queries, even if the output data that the llm gets is effectively noise at best, or confidently incorrect data at worst which was something I was actively dealing with. MSAM doesn’t do this- Every retrieval is confidence gated across four different tiers (high, medium, low, none) based on actual similarity and activation scores when it is found. High confidence returns full data, medium add a caveat, low gives what little it has, but nothing- returns nothing. “I don’t have this” level of nothing.<p>In my own proof of concept development setup (~700 active atoms on a ~$5/month ARM VPS), startup context compresses down to as low as 51 tokens from a 7,327 token markdown baseline. Having full session savings run up to ~89% vs flat file loading.<p>SQLite + FAISS under the hood, pluggable embeddings (NVIDIA NIM, OpenAI, or ONNX for fully local/no API key).<p>Closest project I found was Letta to what I was attempting to create - main differences are MSAM's lifecycle is fully auditable (you can see exactly why something was demoted or forgotten), confidence gating controls output volume, and emotion at encoding is immutable (records what the agent felt when the memory formed, not re-processing memories at retrieval time).<p>This is truly a prototype project without proper datasets, tuning, and testing at scale. I’ve made sure all functions are testable, and include a synthetic dataset to prove basic functionality and information to the dials- (SPEC.md goes deep on the theory and design rationale behind every configurable parameter)<p>If you are also building agents with memory issues and find this useful, or have feedback regarding it- I’m open to discussions.
Show HN: Aether – Background agents that fix bugs in isolated VMs, opens PRs
VMs shut down shortly after the task completes.There's a simple multi-agent setup: a solver proposes the fix, a review agent evaluates the diff, and the fix has to survive re-execution in a clean isolated environment before a PR gets opened. Not claiming formal guarantees here, just requiring the fix to actually execute successfully in a reproducible environment before it touches your repo.Limitations:- Works best on well-tested codebases where "reproduce and verify" is meaningful
Show HN: I open-sourced an AI sales CRM – and the "experience" problem I learned
I funnelled every neuron of sales knowledge I had into it.Today, I open-sourced the whole thing — code, prompts, agents, orchestration - the whole lot.What it does:- Connects to your email and calendar- Automatically analyses every activity- Joins, records and analyses every meeting- Thinks about every deal like a seasoned sales pro and actively drafts the next best action to move the deal forward.- Consumes your files and playbooks and uses it to draft and inform decisions.- Sends, Schedules, D
Show HN: How AI Is Turning Average Posts into Viral Content
I used to post and just hope something hit. No real feedback, no clue why one video did okay and the next one died. Viralize shows you an AI Viral Score before you post, so you actually know if your content has a chance. It breaks down hooks, pacing, retention, and whether the algorithm will even care — and tells you what to fix.<p>If you’re trying to grow for monetization, get brand deals, or promote your own business, this matters. Views = leverage. Attention = money. Once I started using it, my content felt intentional instead of random, and my engagement finally made sense.<p>The craziest part? It’s literally $1 to start. No reason not to test it. If you’re serious about social media marketing, getting paid from content, or growing a business online, I 100% recommend at least trying it. Worst case you’re out a dollar. Best case, it clicks and everything changes.
Show HN: Brevoir The due diligence infrastructure that angel investing never had
Hey HN,<p>I built Brevoir after watching an angel investor I know closely make five figure decisions off a 15 minute pitch and a gut feeling. No research infrastructure, no analyst, just vibes and a deck. Turns out this is basically everyone's problem in early stage investing.<p>The individual investor market exploded over the last decade. The tooling never kept up. What exists was built for institutions with research teams and analyst budgets. Nobody built the infrastructure layer for the person writing their own checks.<p>Brevoir is that layer. Drop in a pitch deck or company URL and the platform runs a full due diligence report across 7 sections: founder background, market validation, competitor mapping, traction signals, risk flags, comparable analysis, and a Brevoir Score. Under 2 minutes. Parallel research agents, best in class OCR and web crawling, and a lot of investment specific logic built on top to make the output decision grade, not just a summary.<p>We also just shipped a full deal management platform on top of it. Kanban pipeline, deal rooms, tags, CSV import. Brevoir is the operating system for how an individual investor runs their entire deal flow from first look to decision.<p>First report is free, no credit card. Would love feedback from anyone who evaluates startups. brevoir.com
"Vic Valentine vs teh Vampires" Book Trailer (Thrillville Press, 2026)
VIC VALENTINE VS. THE VAMPIRES synopsis: Aging, retired private eye Vic Valentine is promised restored youth and eternal life in exchange for his soul by an ethereally sexy woman. Since Vic is agnostic, he figures he's better off taking the deal because he's not sure he even has a soul to bargain with, which would make the sexy woman a sucker in more ways than one. Smitten and bitten, Vic heads to Europe for the first time, looking for his missing wife (whom he suspects is a witch, but a good one) and figures even if he finally finds her, she'd understand why he assumed her worst possible fate for the sake of his own immediate benefit. She doesn't. The sexy woman is a lesbian vampire. Vic falls in love with her anyway. It doesn't go so well. Fall, 2026 https://www.willviharo.com Song: "Night of the Vampire" by The Moontrekkers
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