Surge AI

An AI agent found every expiring rent discount in a commercial real estate workbook. Then it missed three break clauses representing roughly $14 million in forecast rent. The task required following two different paths through a 15-tab workbook. The model followed one and stopped. Knowing when you’re done is part of the job. https://lnkd.in/gJ6Rb_gC

Introducing sudo L7: Can Coding Agents Act Like Staff Engineers? Coding agents are getting very good at solving well-defined problems. But staff engineers do much more than write correct code. They understand unfamiliar systems, make architectural decisions, spot risks the ticket never mentioned, and know when passing the tests isn't enough. sudo L7 measures that judgment. Our new software engineering benchmark contains 60 expert-authored tasks, primarily in private production codebases from real companies, complete with legacy code, technical debt, and messy dependencies. We evaluate eight dimensions of engineering behavior, from functional correctness and engineering craft to practical judgment, thought partnership, and knowing when to push back. The best-performing agents score under ~45%. Coding is only part of the job. See you next cycle. Explore sudo L7: https://lnkd.in/grmJpA4J

introducing sudo L7, our new benchmark for staff-level software engineering. coding agents are getting very good at being L3s. give them a well-defined ticket and they can write excellent code. but a staff engineer is valuable for everything that happens around the code. what should we build? what already exists? what's going to break in prod? what did the tests miss? should we even be doing this? sudo L7 has 60 tasks, mostly in private production repos from real companies, written by engineers who've actually owned these systems. expert rubrics grade everything from functional correctness to engineering craft, architectural judgment, thought partnership, and unnecessary complexity. the best agents succeed on only ~45% of tasks. coding is only part of the job. for now, we're keeping them at idiot genius L3. learn more: https://lnkd.in/g7M9wJyp

Only 2 of 66 runs caught a $25.7M pricing error. One screenshot. One unit mistake. A 100× overstatement. See what else frontier models miss on DAYJOB: Finance: https://lnkd.in/efWEU-Gz

For their Mistral Large 4 release, Mistral ran a blind Surge human eval, with our expert software engineers evaluating five frontier models on coding quality. Le Chonk ranked #1 among open-weight models and #2 overall, behind only Opus 5. Unit tests ask: does it work? Human code review asks: would you merge it? Our evals measures the difference. Working code is the floor; professional taste and judgment are what make it shippable. Congrats to the Mistral team on le Chonk and their big release 🇫🇷

🥖 LGTM, le Chonk 🥖 in their latest Mistral Large 4 release, Mistral ran a blind Surge human eval across five frontier models - professional software engineers comparing five frontier models on coding quality. the big guy finished #2, first among open-weight models and behind only Opus 5. most benchmarks can narrowly measure correctness. but human evals get at the deeper stuff: the professional taste, judgment, and maintainability we look for in the real world as well the difference between passing a set of unit tests vs. getting a "LGTM - ship it" bien joué, le Chonk ! congrats to the entire Mistral team 🇫🇷

A big focus for our team at Pulse is making the data AI agents receive more useful. Huge congrats to the Surge AI team on GDP.xlsx, a strong benchmark testing spreadsheet reasoning across finance, engineering and healthcare. With Ultra 2, our latest model, we’re focused on preserving hidden content, formulas, complete records and visual context within these enterprise documents. We evaluated Pulse’s spreadsheet extraction pipeline using the benchmark’s OpenHands agent harness, providing structured text and image evidence upfront. Our latest reported task-pass rates, compared with our earlier Pulse-assisted results: - Claude Opus 5.5 46.4% (up from 41.4%; +5 percentage points) - GPT-6.1 Sol 37.9% (up from 32.9%; +5 percentage points) - GPT-6 Astra 35.0% (up from 30.0%; +5 percentage points) - Claude Fable 5.1 33.6% (up from 28.6%; +5 percentage points) - Gemini 3.8 Flash 23.6% (up from 18.6%; +5 percentage points) Better data gives agents more to work with, read more in the link below.

they grow up so fast 😊 #1 Riemann-bench #1 Chartography #1 GDP.xlsx congrats, Argon and Google DeepMind! this one’s going on the fridge.

GDP.pdf just got a little brother: introducing GDP.xlsx the world runs on spreadsheets. somewhere in Sheet2!D18 there's a vlookup that changes a $14m decision. GDP.xlsx has 70 real-world tasks across 12 domains. what's hard is figuring out how the workbook even works: → which tab is stale? → what does a red cell mean? → which comment explains the exceptions? → which chart does Finance actually trust? → did you remember to check the termination clauses in Sheet 3? the best frontier agent scores 38.3%. (congrats Gemini 4 Argon!) blog post: surgehq.ai/blog/gdp-xlsx