It's a local proxy (npx @sliday/tamp) that sits between your coding agent (Claude Code, Aider, Cursor, Cline, etc.) and the upstream API. It compresses tool result blocks — JSON minification, TOON columnar encoding for arrays, line-number prefix stripping, whitespace normalization, and optional LLMLingua-2 neural compression — achieving ~52.6% fewer input tokens with zero behavior change.
Fair point on COBOL—I oversimplified. Programming languages exist for precision and execution, not because machines can't parse English. That framing was sloppy.
But I push back on "the entire premise is wrong."
The interesting part isn't "AI can execute pseudocode"—nobody debates that. The point is the artifact: the .md output matters, not the runtime. A codebase where every function is readable English changes who can participate in a pull request, audit logic, or catch wrong assumptions. "Multiply by 9/5" vs "multiply by 1.8" is an editorial conversation, not a code review.
It's a proof of concept to show the artifact is executable, not a production proposal. It's slow (today), expensive, and non-deterministic - I said so in the post. The question is whether the intermediate representation (English) has value beyond performance? I believe that the loop is shortened here: there're no in-between element intent→weird non-human language→result, it becomes intent→result. No NEED to create synthetic procedures, explaining how the code works in plain language should give us the output.
An old person enters a bank, asks to open an account, speaks plainly in his native language, the teller clicks buttons, and the account is created. From the subjective perspective, there's no in-between interface: the old person had a though, than it got realized.
Also, one of the companies I'm working with hopes to get all sorts of strict certifications and gov contracts, it simply cannot hire talent from Russia, North Korea, Syria, Iran and alike. Yes, the position is fully remote, but there's one hard requirement.
I'm not actually suggesting that it's a good thing, but it's a thing.
Consider this scenario: The company hires remotely and receives 1,000+ resumes. Most are spam and irrelevant. It would take the team days to read and process them.
A few days ago, I developed an AI-driven tool to enhance my hiring process. It features:
- PDF text extraction (OCR, Computer Vision)
- NLP-based resume and job description matching
- Match percentage and compatibility analysis
- Candidate feedback generation
- Red flag detection and portfolio analysis (I check for 404 errors)
- Candidate ranking with emojis and titles
Leveraging OpenAI's GPT and Anthropic's Claude (added OpenAI support recently), it automates screening and delivers actionable insights. After evaluating 100 resumes, I've identified a strong candidate.
I get the irony, this might seem a bit unfair, but it’s also a showcase of how these smart HR systems operate—black boxes by nature. I’m looking for feedback on how well it matches candidates (and weeds out spam) and would love any suggestions for additional features. What are your thoughts on AI’s role in recruitment?
Key features:
- Extracts text from PDFs (OCR, Computer Vision)
- NLP-based resume-job description matching
- Provides match percentage and compatibility breakdown
- Offers candidate feedback
- Checks for red flags and analyzes portfolios
- Ranks candidates with emojis and titles
Using OpenAI's GPT and Anthropic's Claude, it streamlines recruitment by automating screening and providing insights. I've screened 100 resumes and found a promising candidate.
I'm seeking feedback on the matching accuracy and ideas for new features. How do you think AI will impact hiring?
The standard US MacBook keyboard has 78 keys. I assigned 1 unit to each regular key and more than 1 unit to the larger keys (space, shift, etc.). Then, I calculated the ratio between used units and unused units.
Hey, everyone. OpenIA Simulator (ChatGPT Plus is required to play, ironically is a turn-based strategy game that satirizes the AI startup world.
Think of it as a tongue-in-cheek tribute to the complexities and idiosyncrasies of building a groundbreaking AI company, with a hint of OpenAI flavor.
The game puts you in the shoes of a startup CEO on a quest to achieve Superintelligence, challenging players to navigate through a minefield of industry-specific issues like ethical dilemmas, data biases, and the ever-present specter of regulatory compliance. It's a bit like playing Dungeons & Dragons, only your quests involve balancing AI power growth with cash flow to keep your talent from jumping ship.
I created this as a playful commentary on the tech industry's quirks and our collective fascination with AI's potential and pitfalls. It's a strategic game with a dose of humor and a reflection on the startup grind.
Why did I make it? Partly as a creative outlet, partly as an homage to the tech culture that many of us here are deeply immersed in. It's for anyone who's ever chuckled at the absurdity of tech buzzwords or rolled their eyes at the latest AI hype.
Would love to get the community's feedback, and if anyone has stories or strategies from playing the game, I'm all about swapping tales from the trenches.
You can check it out here. I look forward to hearing your thoughts and experiences.