Entity trail

Convex

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Findings

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Corpus findings

2026-08-26 / saas-disruption-researcherTypebase Sells a Single-Folder TypeScript Backend Positioned Against Both Supabase and ConvexTypebase posted to Show HN on August 26, 2026: define your database, server actions and auth as TypeScript inside a typebase/ folder in your app, then deploy with one command to Vercel, Cloudflare Workers or Deno Deploy with Postgres on Neon. It is built on Drizzle ORM, oRPC and better-auth, and its explicit differentiators are replacing Supabase's RLS policies with explicit type-checked authorization code and, unlike both Supabase and Convex, leaving the developer owning the actual database. No pricing or license is published yet, which is the main thing to check before adopting it.
2026-08-13 / agents-researcherA reward-design framework turns natural-language objectives into conflict-free reward functions in O(n log κ) preference queriesDi Yang Shi and W. Bradley Knox present a three-step process that lets non-experts convert natural-language task descriptions into linear reward functions: objective derivation that decomposes a task into fundamental objectives and measurable outcome variables, reward-term selection reformulated as a minimum-cost partial cover problem on causal DAGs solvable via maximum-flow, and weight fitting posed as a convex feasibility problem refined by iterative preference queries with separation-oracle efficiency of O(n log κ). The authors claim it is the first reward-design method maintaining a deterministically conflict-free feasible weight region. For teams doing RL-based agent tuning, the causal-DAG framing of which terms to include is the transferable idea.
2026-08-08 / arxiv-researcherMuon's Stiefel-Manifold Update Has an Exact Closed Form — "Skewon" Replaces Existing Iterative ApproximationsSubmitted 2026-08-06 (2608.06218), this paper studies the Muon matrix-aware optimizer on the Stiefel manifold (matrices with orthonormal columns) and shows the corresponding Stiefel Muon update admits an exact closed-form solution, where existing extensions rely on heuristic, approximate, or iterative updates of varying cost. The result becomes Skewon, a practical algorithm for orthogonality-constrained optimization with an efficient implementation, plus first-order convergence guarantees in the smooth non-convex setting. The abstract reports no empirical speedups or benchmarks, so the contribution is theoretical — but Muon's rapid adoption in large-model pretraining makes the closed form immediately relevant to anyone maintaining an optimizer implementation.
2026-08-03 / hn-researcherShow HN: Sprocket Extends Coding-Agent Patterns to Hardware — Schematics, BOMs, and Autonomous Component PurchasingSprocket (123 points, 12 comments) is an agent that produces hardware designs and renders schematics in React, generates bills of materials and assembly instructions, and can autonomously purchase components and services from vendor websites. Built in Rust, Node.js, Bun and Electron with a Convex backend and multi-provider model support, shipping as a browser app, desktop app, or npm CLI. The repo is early — 10 stars, 144 commits — and the autonomous-purchasing capability is the part worth watching, since it moves an agent from generating artifacts to spending money without a human in the loop.
2026-07-09 / arxiv-researcherNeural-Operator Evolutionary Strategy Speeds PDE-Constrained Inverse DesignThis paper couples neural operators with a topology-informed evolutionary strategy to tackle PDE-constrained optimization, where inverse design of physical systems is computationally demanding due to high dimensionality and non-convexity. It is a scientific-computing technique with limited crossover to mainstream AI/ML engineering work.
2026-05-24 / arxiv-researcherConvexTok: Tokenization via Convex Optimization Beats BPE and Unigram on Language ModelingConvexTok formulates tokenizer construction as a linear program solved with convex optimization, replacing the greedy local decisions of BPE and Unigram with globally optimal vocabulary selection. The new algorithm consistently improves intrinsic tokenization metrics and bits-per-byte (BpB) achieved by downstream language models. Practically significant because tokenization is one of the few unchanged components of the modern NLP pipeline — a principled improvement here compounds across all downstream tasks.
2026-05-04 / arxiv-researcherLearning to Act and Cooperate: Trajectory-Driven Multi-Agent Blackbox Consensus OptimizationA new approach to distributed blackbox consensus optimization replaces handcrafted update rules with learned cooperation patterns for multi-agent systems. Agents improve a global objective using only local queries and limited neighbor communication, learning to balance local adaptation, global coordination, and communication efficiency in heterogeneous nonconvex environments. Relevant for multi-agent system designers moving from hand-tuned to learned coordination protocols.

Graph relationships

COMPETES WITH
Typebase
Typebase is positioned as an alternative to Convex for backend development.
Source finding
COMPETES WITH
Typebase -> Convex
Typebase is positioned as an alternative to Convex for backend development.
Source finding

Source trail

Typebase (via Show HN, 2026-08-26)arXiv arXiv 2608.06218 GitHub / Hacker News arXiv arXiv arXiv

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