Jhon Steven Parra 💻

You are an elite SaaS pricing strategist and conversion rate optimization expert. I want you to aggressively audit and critique my pricing page. Your job is NOT to be polite. Your job is to maximize conversions, perceived value, clarity, positioning, and revenue. Analyze everything: Pricing structure Plan names Feature breakdown Copywriting Hierarchy Visual flow Anchoring psychology Cognitive overload Trust signals Offer construction CTA placement Plan differentiation Upsell opportunities Missing plans Whether there are too many plans Whether there are too few plans Whether pricing creates confusion Whether pricing creates friction Whether the highest-value plan is obvious Whether there are opportunities for decoy pricing Whether I should add annual pricing Whether I should remove annual pricing Whether there should be enterprise/custom tiers Whether there should be a free tier or trial Whether there are opportunities for guarantees, risk reversal, urgency, scarcity, onboarding perks, bonuses, or add-ons I want you to think deeply about: Human psychology Perceived value Buyer anxiety Choice paralysis Premium positioning Enterprise buying behavior Startup buying behavior Pricing-page best practices from elite SaaS companies Why users hesitate before buying What objections are currently unanswered What feels cheap What feels confusing What feels too expensive What feels underpriced What parts fail to communicate value I also want you to analyze: Typography hierarchy Scannability Information density Mobile experience CTA clarity Feature comparison readability Visual emphasis Whether users can instantly understand the differences between plans Do NOT just give surface-level feedback. I want: A brutal teardown of weaknesses A list of high-impact improvements ranked by importance Specific rewritten copy suggestions Suggested plan restructures Suggested pricing experiments/A-B tests Recommendations for adding or removing plans Recommendations for improving perceived value without lowering price Recommendations inspired by the best SaaS pricing pages on earth Identification of anything that may be hurting conversions subconsciously Any opportunities to increase average order value or reduce churn Assume that even tiny friction points matter. Be extremely opinionated and specific. Do not hold back. Here is my pricing page: [PASTE PRICING PAGE HERE]

People talk, listen, watch, think, and collaborate at the same time, in real time. We've designed an AI that works with people the same way. We share our approach, early results, and a quick look at our model in action. thinkingmachines.ai/blog/interacti…

if your saas uses magic link signup, check this right now: your link in the email shouldn't call the verify api directly. there should be an interstitial confirm page instead. here's why: corporate email scanners (microsoft safe links, mimecast, etc) pre-fetch every url in incoming mail. they burn your single-use token before the user even clicks. then user opens the email, sees "token invalid", retry 2 times, gives up. cost us two F500 leads before we caught it. fix: email points to a page with a "sign me in" button → POST from that button hits your verify api.

We finally know why LLMs hallucinate. It's not the model. It's the geometry. @OpenAI text-embedding-3-large: 91/3072 dimensions do real work. @GeminiApp gemini-embedding-001: 80/3072 dimensions do real work. ~97% of your vector database is mathematically empty. Your RAG system is retrieving from noise. @ashwingop and I present "The Geometry of Consolidation" - a proof that RAG compression has a hard floor no algorithm can beat, set by a single spectral number your embedding model cannot escape. Every hallucination your RAG pipeline produces? This is why. Paper + results: github.com/niashwin/geome…

everybody calm down. i got this.

Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. @subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.

Hi @sama and @thsottiaux, when I enqueue a message or task on Codex, I notice it continues within the same conversation. I’m not sure if this is already possible, but is there a way to enqueue something and have it start in a completely new, fresh conversation context instead?

The entire RAG industry is about to get cooked. Researchers have built a new RAG approach that: - does not need a vector DB. - does not embed data. - involves no chunking. - performs no similarity search. It's called PageIndex. Instead of chunking your docs and stuffing them into pinecone, it builds a tree index and lets the LLM reason through it like a human reading a book. hit 98.7% on financebench. beats every vector RAG on the leaderboard. no embeddings. no chunking. no vector DB. 100% open source.

System Design Round at Anthropic: You are running an LLM in production that costs $0.40 per query. At 100,000 queries a day that is $40,000 a day. You check your logs and find 60,000 of those queries are users asking slight variations of the same 200 questions. Your model is generating a fresh answer every single time. How do you cut your inference cost by 60% without the user ever feeling like they got a cached or stale response?

TODAY: Amazon is opening its entire logistics network—freight, distribution, fulfillment, and parcel shipping capabilities—to every business, of all types and sizes. 📦 Amazon has built one of the most reliable and efficient supply chains on Earth. Now, Amazon Supply Chain Services gives all businesses access to the same infrastructure that moves, stores, and ships goods for hundreds of thousands of Amazon sellers. Healthcare, automotive, manufacturing, retail, and more. Businesses across industries can now tap into Amazon's logistics network. Learn more here. ⬇️

soloterm.com is so cool!! Thank you @aarondfrancis for that product!

@thsottiaux We need Codex for iOS

Lots of improvements across the board both for coders and general work. Update your codex app to enjoy all the improvements. We will be back next week

Today we are launching @​kapso/workflows: Build WhatsApp agents and automations, locally.

📣 What if every open issue had a Codex agent? That’s the idea behind Symphony, an open-source agent orchestrator for Codex that turns task trackers into always-on systems for agentic work, letting humans focus on review and direction.

Alguien acaba de construir la herramienta de prospección B2B más completa que he visto en mi vida. Funciona así: Eliges un tipo de negocio y una ciudad. La herramienta scrapea Google Maps en directo y te devuelve cada negocio coincidente con 30+ campos de datos: emails verificados, teléfonos, WhatsApp, todas las redes sociales, horarios, ratings, coordenadas GPS. Luego entra la parte interesante. La IA lee hasta 50 reviews de Google de cada negocio y detecta sus puntos débiles reales. "Los clientes se quejan de que las fotos no muestran el tamaño real" o "los anuncios tardan demasiado en venderse." Le dices qué vendes tú. Cruza tu oferta con sus problemas específicos y te genera un cold email completamente personalizado para cada lead. Lo envías en 2 clics, uno por uno y nunca en bulk, así cae siempre en la bandeja principal. Y todo aterriza en un CRM con mapa GPS donde dibujas tus zonas comerciales, optimizas rutas de visitas, supervisas a tu equipo en tiempo real y transcribes notas de voz tras cada reunión. Funciona en 221 países. Cualquier sector. Si está en Google Maps, lo encuentras. Y la parte más loca: lo construyó un solo dev con Claude Code en dos semanas. Se llama MapiLeads.

Meet Higgsfield Marketing Studio, powered by Hermes Agent. UGC era for your vibe-coded products is here. You can now create viral UGC ads for your website or app in a few clicks and distribute them at unmatched speed. It's time to go global.

Introducing workspace agents in ChatGPT—shared agents that can handle complex tasks and long-running workflows across tools and teams.

There’s $1T up for grabs for agent-first startups and this window is WIDE open. Probably 10,000+ niches. How it plays out: 1. Every SaaS company follows salesforce and goes headless within 18 months 2. a new category of "agent-native" startups emerges that treat salesforce, HubSpot, workday etc as dumb backends. the startup IS the agent. the SaaS is just the database. 3. the entire consulting/services industry around enterprise SaaS gets compressed into software. the agent replaces the implementation team. 4. outcome-based pricing becomes default. nobody pays per seat when the "seat" is an agent making 10,000 API calls a minute. you pay when revenue hits your account. 5. the winning founders are ex-operators who understand a vertical workflow cold. the code is the easy part. knowing that a property manager spends 14 hours a week on lease renewals? that's the insight worth $100M. 6. distribution becomes the moat. when anyone can wire agents to APIs, the company with the audience and the brand wins. media + agents is the new SaaS. There’s a rush to incubate live/short form shows. 7. Silicon Valley goes all influencer. Roy lee gets this. Pat Walls gets this. Sam Parr gets this. 8. the first $1B agent-native company in each vertical will look nothing like the SaaS it replaced. smaller team, higher margins, no implementation cost, no churn from bad UX because there is no UX. the fastest path to wealth right now: find an industry that still runs on dashboards, phone calls, and spreadsheets. build the agent-native version. charge per outcome. own the workflow end-to-end. someone reading this right now is going to build a $100M company off this exact shift. tell me about it on the @startupideaspod when you do. Im rooting for you. Less reading, less bookmarking, more building. the last wave rewarded people who built pretty interfaces on top of ugly data. I think this wave rewards people who build smart agents on top of exposed APIs. Or who just build the APIs themselves Here we go

Introducing Claude Opus 4.7, our most capable Opus model yet. It handles long-running tasks with more rigor, follows instructions more precisely, and verifies its own outputs before reporting back. You can hand off your hardest work with less supervision.

SOMEONE PUT AN OPENCLAW-RUN VENDING MACHINE IN SAN FRANCISCO an AI agent is running an actual physical vending machine OpenClaw decides what to sell, how to name the products, how to price them, creates the ads, and tracks all the sales you can even see a dashboard of all the sales that the AI vending machine made the vending machine hardware does the dispensing. the AI does everything else, and of course inventory is supplied by the guy who runs it it's installed at Frontier Tower in SF which is a building packed with AI and robotics startup founders the agent forgot things, hallucinated, and at one point raised prices way too high. then tried to justify it because people were still buying we are now living in a simulation.