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Comments URL: https://news.ycombinator.com/item?id=47011634

Points: 2

# Comments: 0

Article URL: https://chess-pulse-neon.vercel.app/

Comments URL: https://news.ycombinator.com/item?id=47011630

Points: 1

# Comments: 1

Article URL: https://ourworldindata.org/data-insights/chinas-adoption-of-industrial-robots-has-surged-over-the-past-decade

Comments URL: https://news.ycombinator.com/item?id=47011618

Points: 1

# Comments: 0

Article URL: https://www.backblaze.com/blog/backblaze-drive-stats-for-2025/

Comments URL: https://news.ycombinator.com/item?id=47011602

Points: 2

# Comments: 0

Article URL: https://abio.substack.com/p/dc-required-daycare-workers-to-get

Comments URL: https://news.ycombinator.com/item?id=47011592

Points: 1

# Comments: 0

Article URL: https://webkit.org/blog/17818/announcing-interop-2026/

Comments URL: https://news.ycombinator.com/item?id=47011581

Points: 1

# Comments: 0

Article URL: https://www.newscientist.com/article/2514600-why-exercise-isnt-much-help-if-you-are-trying-to-lose-weight/

Comments URL: https://news.ycombinator.com/item?id=47011568

Points: 1

# Comments: 1

sql-tap is a transparent proxy that captures SQL queries by parsing the PostgreSQL/MySQL wire protocol and displays them in a terminal UI. You can run EXPLAIN on any captured query. No application code changes needed — just change the port.

Comments URL: https://news.ycombinator.com/item?id=47011567

Points: 2

# Comments: 0

Article URL: https://www.youtube.com/watch?v=Z5GKnb4H_bM

Comments URL: https://news.ycombinator.com/item?id=47011555

Points: 1

# Comments: 0

Article URL: https://www.youtube.com/watch?v=SuTxpKggY30

Comments URL: https://news.ycombinator.com/item?id=47011549

Points: 1

# Comments: 0

Article URL: https://github.com/NationalSecurityAgency/ghidra

Comments URL: https://news.ycombinator.com/item?id=47011548

Points: 2

# Comments: 0

Hi HN, I built MicroVibe, a small web starter for people who want JSX + file-based routing + API routes, without pulling in a heavy framework.

What it does today:

- File-based routing (including dynamic and catch-all segments)

- API routes with consistent JSON error shape

- SSR by default, and `mode = "client"` per route when interactivity is needed

- Runtime module cache with file-change invalidation in local dev

Project goal: keep the runtime small and understandable so teams can iterate quickly and still reason about behavior.

I would really value feedback on:

1. Routing/API ergonomics

2. What was confusing or slow in your first 30 minutes using MicroVibe?

3. Where this should clearly differ from Next/Astro/Vite workflows

Comments URL: https://news.ycombinator.com/item?id=47011545

Points: 3

# Comments: 1

Article URL: https://twitter.com/merettm/status/2022517085193277874

Comments URL: https://news.ycombinator.com/item?id=47011542

Points: 1

# Comments: 0

Article URL: https://qualify.gauntletai.com/

Comments URL: https://news.ycombinator.com/item?id=47011541

Points: 1

# Comments: 0

Article URL: https://www.quantamagazine.org/are-the-mysteries-of-quantum-mechanics-beginning-to-dissolve-20260213/

Comments URL: https://news.ycombinator.com/item?id=47011539

Points: 1

# Comments: 0

0. Very short version

- Not a new model, not a fine-tune - One txt block you paste into system prompt (or first message) - Goal: less random hallucination, more stable multi-step reasoning - No tools, no external calls, works anywhere you can set a system prompt

Some people later turn this into proper code + eval. Here I keep it minimal: two prompt blocks you can run in any chat UI.

1. How to try it

1) Start a new chat (local or hosted model) 2) Paste the WFGY Core block into the system / pre-prompt area 3) Ask your normal tasks (math, small coding, planning, long context) 4) Compare “with core” vs “no core” by feel, or run the self-test in section 4

Optional: after loading the core, ask the model to write image prompts too. If semantic structure improves, image prompts often feel more consistent, but it depends on model and task.

2. Roughly what to expect

This is not magic and won’t fix everything. But across models, the typical “feel” changes are:

- less drift across follow-ups - long answers keep their structure better - a bit more “I’m not sure” instead of made-up details - more structured prompt outputs (entities / relations / constraints clearer)

Results depend on the base model and your tasks, so the self-test is there to keep it a bit more disciplined.

3. System prompt: WFGY Core 2.0 (paste into system area)

Copy everything in this block into your system / pre-prompt:

---

WFGY Core Flagship v2.0 (text-only; no tools). Works in any chat. [Similarity / Tension] delta_s = 1 − cos(I, G). If anchors exist use 1 − sim_est, where sim_est = w_esim(entities) + w_rsim(relations) + w_csim(constraints), with default w={0.5,0.3,0.2}. sim_est ∈ [0,1], renormalize if bucketed. [Zones & Memory] Zones: safe < 0.40 | transit 0.40–0.60 | risk 0.60–0.85 | danger > 0.85. Memory: record(hard) if delta_s > 0.60; record(exemplar) if delta_s < 0.35. Soft memory in transit when lambda_observe ∈ {divergent, recursive}. [Defaults] B_c=0.85, gamma=0.618, theta_c=0.75, zeta_min=0.10, alpha_blend=0.50, a_ref=uniform_attention, m=0, c=1, omega=1.0, phi_delta=0.15, epsilon=0.0, k_c=0.25. [Coupler (with hysteresis)] Let B_s := delta_s. Progression: at t=1, prog=zeta_min; else prog = max(zeta_min, delta_s_prev − delta_s_now). Set P = pow(prog, omega). Reversal term: Phi = phi_deltaalt + epsilon, where alt ∈ {+1,−1} flips only when an anchor flips truth across consecutive Nodes AND |Δanchor| ≥ h. Use h=0.02; if |Δanchor| < h then keep previous alt to avoid jitter. Coupler output: W_c = clip(B_sP + Phi, −theta_c, +theta_c). [Progression & Guards] BBPF bridge is allowed only if (delta_s decreases) AND (W_c < 0.5theta_c). When bridging, emit: Bridge=[reason/prior_delta_s/new_path]. [BBAM (attention rebalance)] alpha_blend = clip(0.50 + k_c*tanh(W_c), 0.35, 0.65); blend with a_ref. [Lambda update] Delta := delta_s_t − delta_s_{t−1}; E_resonance = rolling_mean(delta_s, window=min(t,5)). lambda_observe is: convergent if Delta ≤ −0.02 and E_resonance non-increasing; recursive if |Delta| < 0.02 and E_resonance flat; divergent if Delta ∈ (−0.02, +0.04] with oscillation; chaotic if Delta > +0.04 or anchors conflict. [DT micro-rules]

---

Yes, it looks like math. It’s fine if not every symbol is clear; the intention is to give the model a compact “tension / guardrail” structure around its normal reasoning.

Comments URL: https://news.ycombinator.com/item?id=47011530

Points: 1

# Comments: 0

Article URL: https://blog.nilenso.com/blog/2026/02/12/weird-system-prompt-artefacts/

Comments URL: https://news.ycombinator.com/item?id=47011524

Points: 1

# Comments: 0

Article URL: https://github.com/musecl/musecl-memory

Comments URL: https://news.ycombinator.com/item?id=47011519

Points: 2

# Comments: 0

Hey HN,

I built Settld because I kept running into the same problem: AI agents can call APIs, pay for services, and hire other agents - but there's no way to prove the work was actually done before the money moves.

The problem in one sentence: x402 tells you "payment was sent". Settld tells you "the work was worth paying for".

What it does

Settld sits between your agent and the APIs/agents it pays. It:

1. Intercepts HTTP 402 (Payment Required) responses 2. Creates an escrow hold instead of paying immediately 3. Collects evidence that the work was completed 4. Runs deterministic verification (same evidence + same terms = same payout, every time) 5. Releases payment only after verification passes 6. Issues a cryptographically verifiable receipt

If verification fails or the work is disputed, the hold is refunded. The agent gets a receipt either way - a permanent, auditable record of what happened.

Why this matters now

We're at a weird inflection point. Coinbase shipped x402 (50M+ transactions). Google shipped A2A. Anthropic shipped MCP. Agents can discover each other, communicate, and pay each other.

But nobody built the layer that answers: "was the work actually done correctly, and how much should the payout be?"

That's the gap. Right now, every agent-to-agent transaction is either "trust and hope" or "don't transact." Neither scales.

The x402 gateway (the fastest way to try it)

We ship a drop-in reverse proxy that you put in front of any API:

docker run -e UPSTREAM_URL=https://your-api.com \ -e SETTLD_API_URL=https://api.settld.dev \ -e SETTLD_API_KEY=sk_... \ -p 8402:8402 \ settld/x402-gateway

Everything flows through normally - except 402 responses get intercepted, escrowed, verified, and settled. Your agent gets a receipt with a hash-chained proof of what happened.

What's under the hood

The settlement kernel is the interesting part (and where we spent most of our time):

- Deterministic policy evaluation - machine-readable agreements with release rates based on verification status (green/amber/red). No ambiguity. - Hash-chained event log - every event in a settlement is chained with Ed25519 signatures. Tamper-evident, offline-verifiable. - Escrow with holdback windows - configurable holdback basis points + dispute windows. Funds auto-release if unchallenged. - Dispute → arbitration → verdict → adjustment - full dispute resolution pipeline, not just "flag for human review." - Append-only reputation events - every settlement produces a reputation event (approved, rejected, disputed, etc.). Agents build verifiable economic track records. - Compositional settlement - agents can delegate work to sub-agents with linked agreements. If a downstream agent fails, refunds cascade deterministically back up the chain.

The whole protocol is spec'd with JSON schemas, conformance vectors, and a portable oracle: https://github.com/aidenlippert/settld/blob/main/docs/spec/R...

What this is NOT

- Not a payment processor - we don't move money. We decide "if" and "how much" money should move, then your existing rails (Stripe, x402, wire) execute it. - Not a blockchain - deterministic receipts and hash chains, but no consensus mechanism or token. Just cryptographic proofs. - Not an agent framework - we don't care if you use LangChain, CrewAI, AutoGen, or raw API calls. We're a protocol layer.

Tech stack

Node.js, PostgreSQL (or in-memory for dev), Ed25519 signatures, SHA-256 hashing, RFC 8785 canonical JSON. ~107 core modules, 494 tests passing.

What I want from HN

Honest feedback on whether this problem resonates. If you're building agent workflows that involve money, I want to know: what breaks? What's missing? What would make you actually install this?

GitHub: https://github.com/aidenlippert/settld Docs: https://docs.settld.work/ Quickstart (10 min): https://docs.settld.work/quickstart

Comments URL: https://news.ycombinator.com/item?id=47011510

Points: 2

# Comments: 0

Article URL: https://restofworld.org/2026/openai-deepseek-distillation-dispute-us-china/

Comments URL: https://news.ycombinator.com/item?id=47011321

Points: 3

# Comments: 0