everyone keeps saying hiring is hesitant or broken right now, so i pulled every finance and accounting ad in boston for the last ten days to see what the numbers actually say...

1,039 total ads

first thing the agency noise is crazy but veery specific.. 39% of senior accountant ads are posted by agencies so not the employers thenmselves.. but if you look at tax it drops to 10%!

second thing which is the crazy part for internal TA, out of 421 direct employer ads that stayed live the whole 10 days a whole 53 had to be redated or reposted

thats 1 in 8 internal teams mostly failing to fill a standard role and having to bump the post

with the market supposedly flooded with candidates, why are 1 in 8 roles sitting empty and getting recycled? are hiring managers just paralyzed trying to find a unicorn or is the active talent pool really so bad right now??

what are you guys actually seeing on your desks?

A local SEO guy told me redressal forms eat half his week and Google reverses it a month later. I said just build the better profile but neither of us had data so I thought i;'ll count it.

Checked five packs the top 15 each: emergency locksmith, Phoenix and London, garage door Phoenix, 24 hour plumber Houston, injury lawyer Phoenix. Maps name vs the website footer or og:site_name, and also displayed address on Street View / shared phone nrs etc.

Nearly half broke guidelines eg 28 of 75. Locksmiths had 8/15 and 7/15, lawyers 5/15, garage door and plumber 4/15 each.

What was crazy was that it seems trades play address games like residential, two brands on one phone, etc. Lawyers had zero address issues and were all name games, "Injury Lawyers" and "- Phoenix Office" on brands that don't use them. And 8 of 30 locksmiths are registered as the keyword itself eg "cheap locksmith" so passes review

For people who manage this for clients, do you still file, what's your removal rate and does anyone track whether the listing comes back?

One of the top locksmith results in London lists its address as "Flat 48".. Slightly annoyed and inspired by a recent post i wanted to know how much a local pack breaks Googles own guidelines if you check every listing

What i did: checked 5 searches and for each listing i compared Maps name to site footer or og:site_name. Also checked street view / phone numbers etc

Results:

• Emergency locksmith, Phoenix: 8 of 15 are breaking guidelines. Seven show a residential address, three have words in the name their site doesn't use
• Emergency locksmith, London: 7 of 15. Five residential, one mail drop, two "different" businesses on one phone number
• Personal injury lawyer, Phoenix: 5 of 15. No address problems but all name games. "Injury Lawyers", "Accident Attorneys", "- Phoenix Office"
• Emergency garage door, Phoenix: 4 of 15. Three residential, one executive suite
• 24 hour plumber, Houston: 4 of 15

28 of 75. What i didnt expect is that the spam type follows the niche service eg. trades play address games, professionals play name games. And the smarter locksmiths have registered the keyword as the actual brand, eg "cheap locksmith" on the website too.. which passes review

Google's filter doesn't seem to catch this.. for those doing this for clients what's te most annoying part of this? Documenting, or waiting for google to fix? And what do you do if the spammers come back a month later with new keywords?

If anyone curious drop a category plus city below i can run the same check

Someone here said last month they spend half the week filing redressal form and the keywords come back a month later so I wanted to see how badi t really is

I did five searches eg emergency locksmith, emergency garage door, 24 hour plumber etc across a few cities. Then i compared maps name to the sites footer or og site_name, and checked address vs street view + any shared phone nrs.

Results:

• Emergency locksmith, Phoenix: 8 of 15 breaking guidelines, mostly residential addresses
• Emergency locksmith London: 7 of 15 breaking, one address says "Flat 48"
• Personal injury lawyer, Phoenix: 5 of 15 with all naming issues ("Injury Lawyers", "- Phoenix Office")
• Emergency garage door, Phoenix: 4 of 15
• 24 hour plumber Houston: 4 of 15

Seems like the trades play address games an lawyers play naming games. The smarter locksmith registered the keyword as the brand ("cheap locksmith" style in 8 of 30) which passes guidelines.. I thought this maybe is a US problem but London is as bad as Phoenix

For people who do this for clients which part eats the time?? finding them, documenting them, or waiting on Google after you file? and what do you do about repeat spammers you refile again?

If anyone curious drop a category plus city below i can run the same check

Heres a followup to the utility rules table some of you have been using (26 utilities then, 58 now)

While researching I saw that utilities publish too optimistic clocks eg PSE&G publishes says 21 business days to approval, 25 more after your inspection cert while Sequachee Valley in TN says written PTO in 5-10 business days etc.. yet most utils publish nothing. Meanwhile i see in this sub lots of posts on "system is ready for 6 weeks already but waiting still"

So I added something to the lookup, every utility row allows you to report your timeline. Put the date you applied and date arrived (or simply blank for still waiting) and you will get the average of everyone on same utility

Links in my profile and in first comment.  Post here your utility and how long did yours take? If your utility is missing let me know I'll fill it in as a new row!

Am a data guy, not a carpenter. A carpenter buddy going out on his own asked me how much to charge so I tried to gather data from last few month. The spread is CRAZY... side work from $30 to $185 hourly with one guy saying "$2.50 to $250/hr depending on how bad/good I am with bidding". someone says 8 to 10 doors a day and immeaditely called BS

I wanna settle this with real numbers so copy paste this and fill it in

Region

Loaded hourly or day rate

One production rate (doors hung/day or LF of base/day or whatever)

Ill merge everything + the numbers from old threads and put the whole thing back here tomorrow as a table in the post

And be straight with me on one thing would you actually use a table like this when bidding, or does everyone just go by feel? If nobody wants it Ill save myself the evening

[ Removed by Reddit on account of violating the content policy. ]

A few days ago I posted a table of what 10 utilities do when they detect DIY backfeed (the "PO CO caught me" thread). A bunch of you asked for your utility and a few of you contributed procedures and enforcement stories, so instead of letting it rot in a reddit table I put the whole thing as a link in my bio.

Whats in it now:

26 utilities including everything requested in the thread (PSE, JCP&L, National Grid NY, PECO, Pepco, CMP, FPL, Avista, AEP Ohio, UGI, BGE, DTE, NV Energy, and my first co-op, Nolin RECC in Kentucky).

Every claim is tagged and documented (from the tariff etc), reported (credible user reports, dated), or being verified (row in progress). wittgensteins-boat suggested adding dates and citations, so every row now has source links straight to the tariff, handbook, or PUC page, plus a last-checked date and a button to flag a stale row

There's also a plug-in solar status strip (live in UT, ME, MD, VA now) and the zero-export notes from the thread as rhat was the majority of qs received

Credit where due the NorthWestern MT row is rwright07's full DIY sequence, the Georgia Power enforcement column is M7451's experience, and the APS detection entry ("they sent a tech when my permitted system STOPPED backfeeding") is AZbees

Free, no ads etc. Same caveats as before eg this is research from tariff documents and reported cases, not legal advice, and utilities change this stuff constantly, so read your own tariff before spending money!!

Which utility should I add next? And if your poco ever caught you (or conspicuously didn't), drop the story, the enforcement column is the hardest part to fill from documents

That thread bugged me cause the answers were all anecdotes..one guy gets a truck sent after a drone flight, another guy backfeeds for a year and his poco never says a word. Soi spent some time getting interconnection handbooks, tariff filings and PUC docs for 10 utilities to see what the written rules and reported cases say

Sharing in case it saves someone a bad surprise...

Utility	How they detect	If they catch you	DIY notes
PG&E (CA)	AMI meters flag reverse flow, aerial/drone line patrols reported by users	(!) letter first, then interconnection application or removal	PTO required before export, owner-builder self install allowed with permits, NEM3 net billing
SCE (CA)	AMI reverse flow	(!)	same as PG&E, NEM3
SDG&E (CA)	AMI reverse flow	(!)	same, NEM3
Duke (NC/FL)	(!)	(!)	(!) monthly netting rider since 2023, check self install rules
Eversource (MA/CT)	(!)	(!)	(!) MA net metering near retail for small systems
Duquesne Light (PA)	(!)	(!)	PA net metering, (!) self install status
Rocky Mountain Power (UT)	(!)	(!)	plug-in systems up to 1.2 kW exempt from interconnection since HB 340
Oncor (TX)	TDU meter data, note some older meters register exports as consumption so you pay for what you send out	(!)	deregulated, buyback depends on your retail provider not Oncor
APS (AZ)	(!)	(!)	RCP export rate, steps down yearly
SRP (AZ)	(!)	(!)	solar customers get moved to specific price plans, check before sizing
PSE (WA)	AMI reverse flow	letter first (reported)	apply via PowerClerk before construction, approval to construct required, credits expire Mar 31 yearly
National Grid (NY)	AMI, statewide SIR portal	(!)	apply via portal before install, no fee <50kW, ~4-8 wks to PTO, CBC charge post-2022, plug-in bill awaiting signature
JCP&L (NJ)	(!)	(!)	Level 1 <10kW free, portal, no energizing before approval to operate, inverter settings file required, 1:1 NEM
PECO (PA)	(!)	(!)	Level 1 <10kW free, owner-install box on PA form, Philly wants PECO app before L&I permit, 1:1 NEM + May 31 cash-out
Pepco (DC/MD)	(!)	(!)	Green Power Connection portal, written approval before install/operate, DC PTO 12-24 wks, MD plug-in legal since May 2026, DC bill pending
CMP (ME)	(!)	(!)	NEB kWh credits, apply + agreement before energizing, plug-in law live July 2026: <420W no electrician, 420-1200W electrician
FPL (FL)	(!)	(!)	Tier 1 <10kW free, no insurance req until >10kW, approval before operating, no plug-in law
NorthWestern (MT)	(!)	(!)	$25 agreement <100kW, homeowner permit + electrician sign-off, export only after NWE gets signed forms (user-reported, 9/2025)
Georgia Power (GA)	(!)	reported tolerant of backfeed while PTO application pending (user report)	(!)
Avista (WA)	(!)	(!)	apply BEFORE purchase/install (their wording), ~20 biz days to approval, self-install acknowledged, <100kW NEM, credits reset Mar 31
AEP Ohio	(!)	(!)	ISA signed before install, field verification after, ~$319 meter fee, exports paid at ~11c generation rate only (post-HB6), size ≤120% of usage
UGI (PA)	(!)	(!)	standard PA PUC Level 1 <10kW, owner-install recognized, 1:1 NEM + annual cash-out, small utility = manual process

edit: added PSE, JCP&L, PECO, PEPCO, CMP, and 5+ more by request

detection/enforcement cells still being filled in, drop reported cases if you have them

(!): verifies the utilities are documented acting on momentary events vs sustained export

A few things that stood out to me

Every single one of the 10 requires a signed interconnection agreement before you energize.. so not after they notice but before you flip the switch. The cheapest order of operations looks like 1) permit 2) interconnection approval 3) buy hardware.several people in past threads learned that in the expensive direction

The "zero export" battery setups are not invisible and grid interactive units chasing load will overshoot for a moment when a big appliance kicks off where AMI meters log those reverse flow intervals, and that seems to be exactly how the guy in the original thread got flagged..

Utah is the odd one out where plug-in setups up to 1.2 kW are legal there without an interconnection agreement since last year, and a bunch of states have copycat bills moving. Everywhere else the old rules apply no matter how small your setup is.

Usual caveats like this is from tariff documents and reported cases, not legal advice, and utilities change this stuff constantly. Treat it as a starting point and read your own tariff!!

I only got through 10. If you want yours, name the utility in the comments and I'll dig it up and add it to the table. Also debating whether to turn this into a free lookup covering every US utility, so tell me if that would be useful or if everyone just calls their poco anyway..

I run a small RV solar business and th Battleborn situation messed me up. I had them on my website, in my quote templates recommended them to customers for two years. Found out about the connection issue stuff through Will Prowse like everybody but by that point ive already quoted them to three clients thatmonth..

I had to pulled their logo off my site the same week but it got me thinking about how reactive I am with this stuff. I basically find out a product has problems the same way my customers do... like reddit, youtube word of mouth and by then Ive already been recommending it.

With all the spec sheet changes and warranty policy updates and firmware issues lawsuits i simply don't have a system for catching any of it. I just kinda hope I see it scroll by in my feed. For 15+ SKUs across like 8 brands thats not great.

Curious if anyone else selling or installing solar gear has dealt with this?? Do you have any process for staying on top of product risk or do you just wing it like me?

Im experimenting with ways to find SaaS ideas and fully realized AI sucks in market research.

If you ask your fav LLM youll usually get the following crap:

• fake confidence
• badly cited source material (claude cant even access reddit?!)
• cant read upvotes or other useful data
• hallucinated problems from 2 years ago
• no way to verify anything

I want the opposite. eg. manually finding niche communities, clustering repeated complaints from the past week, finding the exact comments, and THEN creating SaaS hypotheses.. perfect for a data-driven numbers nerd like myself

The first test I ran was for the construction vertical. Which I know nothing about. But that was the whole pont eg can I find evidence of fresh pains in a vertical I am unfamiliar with?

I scanned, for june 20-27:

Source	Rows
r/Construction	1,920
r/ConstructionManagers	809
r/estimators	422
Total	3,151

The best signal I found was that construction folks seem to struggle tracing estimate numbers back to drawings, PDFs, spreadsheets, assumptions, revisions etc. I made sure to locate exact evidence:

Score	Receipt
53	missing product details had to be pushed to RFI
23	manual page renaming "hurts my soul
15	estimator moves PDF markups into Excel, then can't trace quantities later
7	tool praised because exported items jump back to source page
2	comment explicitly mentions audit trail + Excel export

The best hypothesis i came up with was thos, a lightweight audit trail for construction estimates where every quantity links back to the drawing or spec that produced it.

Much better business hypothesis than hallucinated AI startup ideas.

I posted this elsewhere and one person asked for "3D printing / FPV" vertical

That scan found a different wedge which is that FPV/drone builders seem unsure which 3D-printed parts are actually safe or durable enough vs which should just be bought.

They found it useful because it came with raw comments, scores, and links and none of the hallulu LLM garbage.

Anyway I'm having a lot of fun applying this to obscure industries. What is the most boring vertical you can think of? Comment your niche and I'll find what kind of real complaints are hiding in plain sight.

Im trying to build a portfolio of small boring micro-SaaS products

The hardest part for me was always choosing a problem that is real enough for testing.

I kept trying AI tools for idea research but the output was mostly useless, with crappy pain points, no upvote or context data and stuff like bad sources

I'm a real numbers nerd so I want to see the data so I can to scientific hypothesis testing

So I tried a more manual workflow:

1. pick a boring vertical
2. pull recent Reddit threads+comments directly
3. cluster repeated complaints
4. keep only hypotheses with exact comments, scores, and links
5. use the result to decide what to test next

I ran my first run on the Construction vertical

I dont know much about construction but that was the whole point. I wanted to see if the data would surface anything interesting in a field I am not familiar (on purpose)

The data I got:

Source	Rows
r/Construction	1,920
r/ConstructionManagers	809
r/estimators	422
Total	3,151

The cleanest signal I found was pretty specific

People who estimate construction costs seem to struggle with tracing numbers back to drawings, PDFs, spreadsheets, assumptions, revisions.

The evidence I found to support this hypothesis:

Score	Evidence
53	Real takeoff where missing product details had to be pushed to RFI
23	Manual page renaming “hurts my soul”
15	Estimator marks up PDFs, moves numbers into Excel, then struggles to find where quantities came from later

The business I think can be built is a lightweight audit trail for construction estimates where every Excel quantity links back to the drawing/spec/assumption that produced it

Now this is probably not a real validated business idea but its e a better starting point than "AI gave me 10 startup ideas"

The next test would be going into construction communities and asking something narrower like if they struggle with how to trace where their estimates / quantities come from

Can this workflow produce better starting hypotheses before I waste time building or interviewing around random ideas?

I can run a few other boring niches through the same process for fun

Comment a niche / vertical and Ill reply with a small scan like 1–2 business hypotheses with exact evidence, scores, and links

I'm testing a tiny validation workflow

My problem is before doing customer interviews, I often don't even know which problem hypotheses are worth testing.

And asking AI for “startup ideas” is usually garbage because it

• misses source material
• invents patterns
• can't show clean evidence

So I tried a more scientific approach:

1. pick a boring vertical
2. pull Reddit threads/comments directly
3. cluster repeated complaints
4. keep only hypotheses with evidence like exact comments, scores, links, thread context
5. use those hypotheses as inputs for interviews / validation posts

My first test was for the construction vertical:

Source	Rows
r/Construction	1,920
r/ConstructionManagers	809
r/estimators	422
Total	3,151

The cleanest signal i got was

>Construction estimators may struggle to trace estimate numbers back to drawings, PDFs, spreadsheets, assumptions, and later revisions

With some sample evidence

Score	Evidence
53	Real takeoff where missing product details had to be pushed to RFI
23	Manual page renaming “hurts my soul”
15	Estimator marks up PDFs, moves numbers into Excel, then struggles to find where quantities came from later

The main hypothesis igot out of this was

>Estimators need a lightweight audit trail where every quantity links back to the drawing/spec/assumption that produced it

Now im not claiming this is validated though it feels like a much better starting point than "AI gave me 10 SaaS ideas"

Curious, would you consider this useful pre-validation evidence or still fake signal until direct interviews/sales?

Also happy to run a few boring verticals through this.

Comment a vertical/niche and ill reply with 1–2 evidence-backed hypotheses with exact comments, scores, and links

No matter how you prompt your fav AI, it comes up with some bullshit microsaas ideas. Claude doesn't even have access to reddit, and Gemini just scrapes random text from "archive snapshots" meaning things like upvotes etc are totally lost.

In general AI does three bad things when you try to research new microsaas ideas:

1. misses source material
2. invents patterns
3. can’t show evidence for why a pain is real

Why should you care about pain points from subreddits? Because obscure pain points are a gold mine for building hyper-niche microsaases.

So I built a small workflow that pulls the top subreddit threads/comments directly and turns them into evidence-backed pain hypotheses based on real data.

Every hypothesis needs:

• exact comments
• scores/upvotes
• permalinks
• thread context
• a CSV evidence trail

I did my first test run on a random topic: construction.

I don't have a connection to construction I just wanted to check a boring vertical with real workflows.

All based on the top posts between 20th - 27th June from the below subreddits:

Subreddit	Rows
r/Construction	1,920
r/ConstructionManagers	809
r/estimators	422
Total	3,151

Top signals

Rank	Pain hypothesis	Evidence	Max score	Confidence
1	People doing construction cost estimates struggle to trace numbers back to drawings, assumptions, PDFs, and spreadsheets	137 rows / 14 threads	53	High
2	Construction people are skeptical of AI tools unless outputs link back to source documents	72 rows / 7 threads	86	High
3	Small contractors lack a clean way to track subcontractor pricing/history	15 rows / 1 thread	13	Medium
4	Public bid submissions still involve weird paperwork, copies, deadlines, and hand-delivery rules	63 rows / 3 threads	105	Medium
5	Construction managers complain about meetings, RFIs, change orders, paperwork, and burnout	219 rows / 17 threads	65	Medium-low

The most interesting one was #1.

I don't even know what an estimator is, but at least I found some interesting patterns:

Estimators take construction drawings/specs, calculate quantities/costs, move stuff between PDFs/software/Excel, and later need to explain where every number came from.

Evidence examples:

Score	Evidence
53	User posts a real takeoff where missing product details had to be pushed to RFI.
23	Commenter reacts to manual page renaming: “hurts my soul.”
15	Self-taught estimator says they mark up PDFs, move numbers into Excel, then struggle to find where quantities came from later.
11	Advice: name quantities with section/detail/size or you’ll forget what they mean.
7	Bluebeam recommended because clicking an exported item can jump back to the page/highlight.
2	Tool recommendation explicitly mentions “audit trail” + Excel export.
1	Commenter says every quantity should track back to plans, specs, or an assumption.

After some more investigation this was the most clear pain point + hypothesis business I found:

>“Audit trail for construction estimates: every Excel quantity links back to the drawing/spec/comment/assumption that produced it.”

Not claiming this is a good business.

Just saying this is the kind of evidence-backed hypothesis I find more useful than generic “AI found 10 SaaS ideas” bs

I’m mainly building this for my own PMF hunting, but I can run a few other niches through it.

Drop 3/4 subreddits for a vertical you care about and I’ll reply with a mini-scan with 1/2 pain hypotheses, exact evidence, scores, and links

I built a small SaaS on the side mostly with Claude. It makes some money and then someone slid into my DMs about buying it.. i didn't expected that

Then they asked to see the code just to check and I kind of just froze. I don't want to send my repo to a stranger who could rebuilt it and ghost me and half the people poking around arent even serious. But also honestly am not sure I could walk them through the architecture ifi tried, because I didnt exactly code it by hand

So I'm stuck cause i won't give repo access but i cant really prove it's solid anyway.

For anyone who's sold a side project when the buyer wanted to see the code, what did you do? am not looking for "put together a diligence pack" ... thats a ton of work for a small sale and i doubt most people really bother, so looking more for what you did in practice

Hand over the repo and hope theyre decent? refuse and lose the deal? or something in the middle like a call, a writeup, some stats, partial access to show it's not a mess without opening up the whole thing? dd it actually work?

Built most of this thing with AI over the last few months. It works, it makes some money and I'm happy with it but maybe a bit bored with it. Had someone who was interested in buying the business poke around recently and got the usual q's on revenue/churn but the q that surprised me was "how do I know this doesn't just fall apart in six months in a puddle of slop"

Honestly am not sure I could answer it. A lot of the code was written by Claude and I couldn't walk you through the architecture if my life depended on it. It runs just fine tough

Feels like a problem specific to this whole vibecoded wave. Buyers are starting to assume anything AI-built is spaghetti until proven otherwise, and the annoying part is I can't fully prove otherwise either

For anyone who's sold something they built mostly with AI HOW did you get the buyer comfortable it won't rot?? Did they actually check, or just take your word and did it cost you on price?

Right now my answer to this is a fat shrug

I've been picking up small AI-built apps as a portfolio like small stuff on Acquire, Flippa, in DMs etc

A lot of it is good now as one person can ship something real in a weekend, and some have nice cash flows. The hard part is telling those from the dead ones before I make an offer. Plenty of what's listed are AI slop codebase generated 6 mos ago and never touched since.

At these prices you can't hire someone to review the code, and no seller hands over the repo before youve put anything on the table. Fine

What I've started doing is asking the seller to run quick CLI one liners (basically git log + dependency stats) and paste the output. Under a minute, exposes none of the actual code, and it at least tells me whether anyone's pushed a meaningful commit in 6 months, and whether the dependencies are ancient etc. Wont tell me the code is good but at least i get a rundown on some stats

Thinking about making it dumb simple like I send a link, seller copy pastes the output of a few one-liners in their terminal and I get a plain-English read.

Would you actually pay ~$49 to triage a deal this way, or not worth it at these price points?

And the part I can't judge: would a seller actually take a minute to copy paste the output of a few CLI lines, or complain the same way they do over repo access?

I look at a lot of SaaS and tech-enabled businesses. The biggest pre-LOI headache doesnt seem to be the financials but rather the codebase. You want to know if you're buying a half-abandoned repo or a one-dev house of cards, but no sane seller is giving you repo access before you sign anything....

But I realized you don't actually need their code to figure this out. You just need the metadata.

Lately, I’ve been asking sellers to run a quick read-only script in their terminal (basically just git log and dependency stats). It takes them less than a minute, exposes zero IP, and immediately flags the biggest risks:

• Developer concentration eg. did one guy write 95% of the codebase? (Massive key-man risk)
• Code rot eg. has anyone actually pushed a meaningful update in the last 6 months?
• Tech debt eg. are the core dependencies three major versions behind?

I'm thinking about packaging this into a dumb-simple tool: Buyer sends a link, seller runs a 1-liner, buyer gets a plain-English risk report. No source code ever leaves their machine.

Two questions for the community..

Buyers: would you actually use this to triage deals pre-LOI? Is it worth paying ~$49 a pop to avoid a nightmare codebase?

The real test: would your sellers actually run a 1-minute script, or do you think they'd still balk the exact same way they do for repo access?

Curious if anyone has tried something similar or if sellers just get too defensive.

First-time buyer here mostly looking at SaaS and tech-enabled businesses. Same pre-LOI wall a lot of you hit on financials except mine is the codebase.

I can pressure-test the P&L and add-backs fine. What I can't see before signing is whether the tech itself is a liability eg is it one developer holding the whole thing together? Is it built on a framework three versions out of date? Infra costs about to balloon as it grows? Sellers won't give repo access to someone who hasn't put money down..

So I get together outside signals: code commit history, cloud bill, a dependency list, a short technical questionnaire w/ NDA. It's rough and I redo it from scratch every deal.

For anyone who's bought a software or tech-enabled business: how did you get comfortable with the tech before LOI? Skip it, pay a firm (what did it run you?), or just price it in?

And for those buying physical or service businesses: is tech even on your radar, or a non-issue next to financials and owner dependency?

Trying to figure out if this is a real problem or just me overthinking it because my targets are tech/code-heavy...

R01 resubmission is two weeks out, the science was done, the aims were done, and the weekend still got eaten by editing line items and rewording the same equipment description four times because NIH wants one framing and my pre-award office wants another... plus a solid hour figuring out where a $4k software license should be filed

A colleague last cycle got returned without review because his budget listed effort in calendar months in one place and percent in another, so six months of work came back over formatting, not rejected but returned which is somehow worse.

The maddening part is that NSF doesn't want what NIH wants, Horizon Europe is a different beast, and DOE has its own thing, so every one of them could be a template with checkboxes and instead it's a folk tradition passed PI to PI..

Two questions:

How many hours per submission are you spending on budget justification?

Second, do you have a system you'd share, like a template, a trained grad student, or a friend in pre-award you bribe with coffee??

Drop in comments or DM me, i'm happy to trade what i have for yours

And if you've ever been returned over a budget formatting issue please tell me which agency and what went wrong, because i want to know if this is an NIH problem, everyone problem, or me problem