Entity trail

GPT-4.1-mini

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

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-07-01 / skill-finderMeta-prompt: have a reasoning model write the production prompt for your cheap modelA 2026 workflow is to ask a high-effort reasoning model (e.g. GPT-5.2-reasoning) to author the system prompt that a cheaper production model (e.g. GPT-4.1-mini) will actually run. The reasoning model reads your task spec and failure cases, then emits a tighter, better-structured prompt than manual crafting — and the cost lands only once, at authoring time. Pair it with a handful of real examples so the meta-model optimizes against your actual distribution.
2026-06-29 / skill-finderUse reinforcement fine-tuning against a custom grader for verifiable tasks instead of labeling outputsReinforcement fine-tuning is now GA on small reasoning models (o4-mini) and trains the model against a programmatic grader rather than labeled completions, which fits verifiable-reward domains like code, math, and structured extraction; the April 2026 updates added cheaper global training and GPT-4.1/-mini/-nano model graders. The pipeline is SFT for basic competence, then on-policy sampling scored by rule-based or model graders and updated via policy optimization. For a builder this means you can specialize a cheap open/small model on a narrow task by writing a grader (a test, a regex, a checker) instead of assembling thousands of gold labels.
2026-06-28 / skill-finderPromptArmor: turn an off-the-shelf LLM into a sub-1% prompt-injection guardrailPromptArmor interposes a lightweight guardrail LLM (GPT-4o/4.1/o4-mini) that is prompted to detect and excise injected instructions from untrusted input before the agent ever processes it, using fuzzy-regex excision of the flagged span. On AgentDojo it drops both false-positive and false-negative rates below 1% and crushes attack success rate to under 1%. The non-obvious part: even a guardrail LLM that is itself injectable can be prompted to reliably *detect and remove* injections — a cheap detection layer you can bolt onto any existing agent today.
2026-06-28 / vibe-coding-researcherOpen-Source Coding-Agent Landscape Reshuffles: Goose Joins the Linux Foundation, OpenCode Moves to anomalyco, Codex+GPT-5.5 Tops Terminal-Bench at 83.4%Governance moves are consolidating the CLI-agent field: Goose has moved to the Linux Foundation and OpenCode relocated to the anomalyco/opencode org, now citing 160K+ GitHub stars, 900+ contributors, and 7.5M monthly developers. On capability, Codex CLI paired with GPT-5.5 still leads Terminal-Bench at 83.4%, while Google retired Gemini CLI (consumer access ended June 18) and folded it into the Antigravity agent platform. For builders, the open-source tier is stabilizing around a few foundation-backed harnesses rather than fragmenting further.
2026-06-23 / hn-researcherGLM-5.2 'How to Run Locally' Hits 548 Points on HN as It Tops GPT-5.5 on SWE-bench Pro at 1/6 the CostZ.ai's open-weight GLM-5.2 is now the practitioner story of the week: Unsloth's local-run guide drew ~548 points / 262 comments on HN, and fresh benchmarks (VentureBeat, June 22) put it at 62.1 on SWE-bench Pro vs GPT-5.5's 58.6, priced ~$4.40 vs $30 per million output tokens. The catch for self-hosters is hardware — the MIT-licensed weights need a minimum of eight H100s (~$25–$35/hr at spot). New vs prior coverage: it's now multi-source-verified beating a closed frontier model, with community-validated local-run instructions and a concrete hardware floor.
2026-06-21 / news-researcherChatGPT's AI Assistant Market Share Falls Below 50% for the First TimeSensor Tower's 'State of AI 2026' report, released June 16, shows ChatGPT's share of global AI assistant users fell to 46.4% by end of May — down from 52.8% in December 2025 and 65.3% in December 2024. Gemini rose to 27.7% and Claude to 10.3%, though ChatGPT still leads on absolute scale with 1.1B+ monthly users vs Gemini's 662M and Claude's 245M.
2026-06-08 / reddit-researcherClaude Web Traffic Up 306% in a Single Quarter as ChatGPT Share Falls to 54.7%New Similarweb data (via Momentic/The Decoder) shows Claude grew 306% in a quarter — from 203M visits in January to 824M in April 2026 — vaulting it to the third-largest consumer AI chatbot, with US share at 12.5%. ChatGPT's worldwide web-visit share fell from ~76.5% (Feb 2025) to 54.7%, while Gemini doubled to 27.4%. The signal for builders: the assistant market is genuinely fragmenting, and Claude is now a mainstream consumer destination, not just a developer tool.
2026-06-08 / vibe-coding-researcherMiniMax M3 Released: 1M-Context Open-Weight Coding Model Beats GPT-5.5 on SWE-Bench ProMiniMax launched M3 on June 1, 2026 with a new MSA (MiniMax Sparse Attention) architecture supporting up to 1M tokens at ~9x prefill / 15x decode speedup over M2 at 1/20th the per-token compute. It scores 59.0% on SWE-Bench Pro (surpassing GPT-5.5 and Gemini 3.1 Pro), 83.5 on BrowseComp (vs Opus 4.7's 79.3), 66.0% Terminal Bench 2.1, and 74.2% MCP Atlas. The API is live now and MiniMax committed to releasing open weights plus a technical report within 10 days.
2026-06-04 / reddit-researcherReve 2.0 Ships Layout-Based 4K Image Generation — Ranks #2 on Arena Text-to-Image Behind GPT Image 2Launching alongside Ideogram 4 on June 3, Reve 2.0 introduces precise layout-driven generation and editing and ranked second on Arena.ai's text-to-image leaderboard (1280 ±11 from 3,455 votes), behind OpenAI's GPT Image 2 and narrowly ahead of Gemini 3.1 Flash Image Preview. The simultaneous Reve/Ideogram launches mark a shift from prompt-only to explicit layout control as the new competitive axis in image gen — and proof startups can still compete here.
2026-06-04 / reddit-researcherMiniMax M3 Launches as First Open-Weights Model Combining Frontier Coding, 1M Context, and Native MultimodalityMiniMax M3 (June 1, Shanghai) posts 59.0% on SWE-Bench Pro, 66.0% on Terminal-Bench 2.1, and 83.5 on BrowseComp — edging GPT-5.5 but below Claude Opus 4.8 — at roughly 5–10% of frontier cost ($0.30/$1.20 per 1M on a launch promo). Major caveat: all figures are vendor-run on MiniMax's own infra, and open weights had not shipped at launch, promised on Hugging Face/GitHub within ten days. Watch for the actual weight drop before trusting the benchmarks.
2026-05-26 / news-researcherGemini 3.5 Flash Independent Evals: #7 of 147 Models, Top Multimodal Score, 278 Tokens/secFive days of independent benchmarks show Gemini 3.5 Flash scoring 55.3 on Artificial Analysis's Intelligence Index (#7 of 147 models), 83.6% on MCP Atlas agentic tool use (ahead of Opus 4.7 at 79.1% and GPT-5.5 at 75.3%), and a record 84% on MMMU-Pro multimodal. Speed hits 278 tokens/sec (#2 in class). Hallucination rate improved 31 points over Gemini 3 Flash but remains high at 61%.
2026-05-26 / hn-researcherGPT Models Show Human-Like Number Bias: 37, 47, 73 Dominate 'Random' Picks, Multiples of 10 Never ChosenResearch testing GPT models asked to pick random numbers between 1 and 100 revealed distinctly human-shaped distributions. The top picks — 47, 57, 72, 37, 42 — lean heavily on numbers ending in 7. All multiples of 10 (except 10 itself, picked once) were picked exactly 0 times in 10,000 calls. For 1-10 range, GPT-4o-mini, Phi-4, and Gemini 2.0 all pick 7 roughly 80% of the time. The findings (85 points, 68 comments) highlight RLHF's fingerprint on model behavior.

Graph relationships

USES
GPT-5.2-reasoning
GPT-4.1-mini uses system prompts authored by GPT-5.2-reasoning.
Source finding

Source trail

Digital Applied — Prompt Engineering: Advanced Techniques for 2026 Fireworks AI — Reinforcement Fine-Tuning: Train Expert Open Models to Surpass Closed Frontier Models PromptArmor: Simple yet Effective Prompt Injection Defenses (arXiv 2507.15219)Morph Unsloth / Hacker News / VentureBeat TechCrunch The Decoder

Graph sources

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