Bug-attribution linter for AI-generated PRs on large, multi-file diffs
A CI-integrated review tool that flags which specific AI-generated hunks in a large PR are most likely to contain production-risk bugs (missing error handling, hardcoded secrets, hallucinated calls), so a human reviewer knows where to spend their limited attention.
One serious risk stands between this and a build call — An incumbent can ship this as a feature — GitHub, Claude, Codex and other incumbents already sit inside the exact PR/diff workflow this idea targets and could ship diff-level risk flagging as a feature rather than a standalone product.Evaluated Aug 14, 2026 · thresholds published at /methodology
Supporting evidence4
Reviewers explicitly report they cannot read 20-30k line AI-generated diffs and default to rubber-stamping, meaning attention-routing (not full review) is the actual unmet need.
A specific, quantified defect rate (43% of AI PRs contain at least one bug that passes review) gives a concrete target for a detection tool to beat.
Multiple independent complaints describe the same failure mode of large, sprawling AI diffs with hidden bugs (hardcoded secrets, broken integrations, hallucinated docs) across different teams and timeframes, not a single anecdote.
Teams already pay for adjacent workflow tools (local agentic coding sessions, defect-finding assistants) showing willingness to spend on AI-code-quality tooling specifically.
Falsifying evidence4
GitHub, Claude, Codex and other incumbents already sit inside the exact PR/diff workflow this idea targets and could ship diff-level risk flagging as a feature rather than a standalone product.
General-purpose LLMs (ChatGPT, LLMs broadly) are already being used ad hoc to review and explain suspect code, which may be 'good enough' for many teams without a dedicated product.
None of the 7 named products in this cluster have verified revenue, so there is no evidence anyone has proven a paid model for this exact workflow works.
The cluster mixes many distinct problems (review fatigue, secrets leakage, IaC threat modeling, SBOM generation, comment clutter) rather than converging on one workflow, so it is unclear the 30 signals actually describe demand for a single product.
Most likely cause of death
The most likely failure is that this becomes a feature request absorbed by GitHub or the coding-agent vendors (Claude/Codex) rather than a standalone purchase — teams already route review through those tools, and a bolt-on diff-risk flagger has no moat once the incumbent adds 'confidence score per hunk' to its own PR view. Defensibility would need to come from a proprietary defect dataset (e.g. verified patterns behind the 43% bug-rate claim) or deep integration with a specific stack that incumbents are slow to serve, neither of which the evidence here establishes.
Demand ladder
A complaint is not a customer. Weighted ×1 / ×3 / ×8 / ×15.
Verified revenue: none on file for this problem yet. That is an absence of records, not proof nobody is earning here.
Momentum
Is this problem getting louder or quieter?
Saturation
How many people are already on it. Most sites hide this.
Problem evidence
Who feels this, how often, and why what they use today does not fix it.
- Who feels it
- Senior engineers and tech leads at teams that have adopted agentic coding (Claude Code, Cursor, Copilot, Codex) and are now receiving multi-file, multi-thousand-line PRs they are nominally responsible for approving. Secondary: the engineering manager who owns production incidents and cannot see where quality is degrading.
- How often
- Per PR, several times a week. Signals describe 5 PRs a day as a fatigue threshold (S-122) and 20–30k line diffs arriving 'all the time' (S-460). Cluster momentum is accelerating (30d 14.5 vs 90d 6.2), with the newest signals dated 2026-08-13.
- Why current fixes fail
- The failure point is the moment a reviewer opens a 20k-line agent PR in the GitHub diff view on a Tuesday afternoon. GitHub orders hunks by file path, not by risk, so there is no signal about which of 140 hunks deserves the 40 minutes the reviewer actually has; the rational move is to type 'lgtm' (S-460), and at least one team has formalised that into policy — engineers are told to click approve without reading (S-219). Ad hoc LLM review (X-213) fails on the same axis, not on quality: it requires copy-pasting chunks out of the diff, it returns evenly-weighted commentary on everything, and it produces no artifact the next reviewer can trust. Existing AI review bots make it worse by adding a second triage queue of per-hunk comments, which is exactly what senior engineers say they will not accept (S-18). Secret scanners catch credentials but nothing catches 'this hunk calls a method that does not exist' or 'this hunk swallows the error path' (S-2083, S-44).
Reviewers cannot meaningfully review the size of diff agentic tools now produce, and respond by approving unread — one engineer reports being instructed not to read PRs at all, another that 20–30k line changes end with 'sure buddy lgtm'.
AI-generated code volume has moved the bottleneck to review and is producing measurable reviewer fatigue among senior staff.
The defect classes this idea targets are the ones users name: missing error handling, hardcoded secrets/credential leaks, hallucinated calls and documentation reaching deployment.
The specific '43% of AI-generated PRs contain at least one bug that passes code review' figure comes from a Product Hunt launch claim in this cluster, not from an independently verified study in this block.
After-the-fact attribution is felt as a distinct pain: with multiple files changed by an assistant, finding which change caused a later break is described as harder than the fix itself. This rests on one Product Hunt signal.
Teams explicitly ask for review tooling with structural diff analysis and Critique-level sophistication, and name GitHub's PR review as lacklustre.
Managers lack visibility into AI-code quality decay and therefore cannot get refactor work prioritised.
There is a stated allergy in the target buyer population to AI reviewing AI — 'using AI to automate code reviews is not acceptable' — which constrains how the output can be framed and delivered.
Who buys it
The person who feels the pain and the person who signs are rarely the same.
- User
- Senior engineer / tech lead assigned as reviewer on agent-generated PRs; secondarily the author of the agent PR who wants it merged.
- Buyer
- Engineering manager or VP Engineering of a 10–60 developer org that has already standardised on a coding agent. At the smallest end (S-2108-style AI-built startups), the founder/CTO signs personally.
- Pain owner
- The engineering manager: they own the on-call consequences of unreviewed merges and are the one who cannot currently justify refactor time (S-2980).
- Budget source
- Existing developer-tools line item that already carries Copilot/Cursor/Claude Code seats; secondarily the security/AppSec tooling budget where secrets and vulnerability exposure are the framing (S-2527, S-44).
- Urgency
- Urgency is real but not deadline-shaped. The forcing events visible in this block are (a) an incident traced to an unread agent PR, (b) a manager needing to argue for a review/refactor sprint (S-2980), (c) a team formalising approve-without-reading and someone objecting (S-219).
- Already spending on
- Claude CodeCursorCopilotGitHubWindsurfChatGPTCodex
Product concept and MVP
Two versions: the one you deliver by hand first, and the one you build.
A CI check that reads a PR diff, splits it into hunks, and returns one ranked artifact: the 5–15 hunks most likely to carry production risk, each with a named reason (unhandled error path, hardcoded credential, call to a symbol that does not exist in the repo, silent behaviour change in a hot path). The output is a reviewer's attention budget, not another comment queue.
Concierge version
No software. Ten users, done by hand. This is how you find out you are wrong for the price of a weekend.
Fully fakeable, and should be faked. Get read access to 10 teams' repos via a GitHub App with a webhook that emails the founder when a PR over ~400 changed lines opens. The founder (plus an LLM used privately, plus grep for secrets and a tree-sitter symbol check run locally) hand-produces a one-page 'review here first' note within 3 working hours and posts it as a single PR comment: ranked hunks, reason per hunk, and an explicit 'these 90% of hunks are low risk, skim them'. No dashboard, no scoring model, no account system. Measure: did the reviewer open the flagged hunks first, did they find the flagged issue real, and would they pay for the note.
Vibe-coded version
What a build platform can scaffold, and what you write yourself.
GitHub App + GitHub Actions check. Parse the diff, use tree-sitter to map each hunk to a function/symbol, run three deterministic detectors (secret-shaped literals, error paths that return/swallow without logging, calls to symbols not resolvable in the repo or lockfile) plus one LLM pass constrained to output a rank and a one-line reason per flagged hunk. Render as a single collapsible check summary with deep links to hunks. Store nothing but hunk hashes and verdicts.
Must have
- Ranked hunk list capped at 15 items, with an explicit 'the rest is low risk' statement
- Reason per flag drawn from a fixed taxonomy, so the reviewer can dismiss a class of noise once
- Deterministic detectors for secrets and unresolvable symbol calls (no LLM required to justify these)
- Single check output, one comment maximum — never one comment per hunk
- Works on diffs above 5k lines without truncating silently
- Feedback capture: reviewer marks each flag real / not real, stored as labelled data
Nice to have
- Agent provenance: label hunks by which tool authored them via commit trailers
- Repo-specific tuning from accepted/dismissed history
- Manager weekly digest of flagged-vs-merged-unreviewed counts
Not yet
- Auto-fix or suggested-patch generation — turns you into a code writer and puts you head-on against Claude Code
- Self-hosted / air-gapped deployment (S-1860 demand exists; it kills iteration speed in month two)
- GitLab and Bitbucket support
- IaC threat modelling and SBOM generation (S-321, S-307) — different buyer, different product
- Comment-clutter and dead-code cleanup (S-234)
- Test-selection / pipeline-speed work (S-120)
- Integrations
- GitHub Checks API + PR API (GitHub App) · GitHub Actions · tree-sitter for hunk-to-symbol mapping · Package lockfiles for symbol/dependency resolution · Slack (single channel notification of high-risk PRs)
- Build difficulty
- 3/5 — The plumbing (GitHub App, diff parsing, tree-sitter, check rendering) is a few weeks. The hard part is precision: at 15 flags per PR, two false positives per PR is enough for a fatigued senior engineer to mute the check. There is no defect-labelled dataset in this block, so the first version's accuracy rests on hand-tuned detectors plus whatever labels the concierge phase produces.
Competitors and alternatives
Including the free workaround people use today, which is usually the real competitor.
Direct
- No product with recorded revenue or pricing exists in this block — the products list is empty (X-214).
Indirect
- GitHub PR review (the default surface, named in 4 signals)
- Claude / Claude Code and Codex used to review their own or each other's output
- Copilot and Cursor in-editor review
- Critique and Phabricator as the referenced 'good' review tooling
- CRUX (Amazon internal)
Workarounds
- Approve without reading — formalised as policy in at least one org (S-219) and as a coping habit in another (S-460)
- Paste the suspect file or hunk into ChatGPT/Claude and ask what is wrong (X-213)
- Push back on the author and make them re-do it, 30–50% of the time (S-346)
- Stop the sprint and spend a few days manually reviewing and rewriting (S-2980)
- Round-robin / 'can someone review my PR' in team chat, which nobody picks up (S-524)
- Hire out the cleanup: paid help for founders whose AI-built product is insecure or will not deploy (S-2108)
| Product | Customer | Pricing | Strengths | Weaknesses | Gap |
|---|---|---|---|---|---|
| GitHub (PR review) | Every team in this cluster | Not recorded in this block | Owns the diff surface; zero adoption cost; already in the workflow | Called 'incredibly lackluster' by users; orders hunks by path not risk; no structural diff analysis for large agent PRs (S-408, S-2303) | Attention ranking inside a huge diff. Also the single largest risk: this is a plausible GitHub feature (X-212). |
| Claude Code / Claude | Developers generating and reviewing the diffs (6 and 5 signals) | Not recorded in this block | Already authorised on the repo; can explain any hunk on demand | Reviews what you paste, gives no prioritisation across 140 hunks, and is the same class of system that produced the diff — engineers distrust the loop (S-18) | An independent, deterministic layer whose output is a ranked short list rather than prose. |
| Codex / Copilot / Cursor | Same developers, in-editor | Not recorded in this block | Inside the authoring loop; can prevent some defects before the PR exists | No view of the merged-PR review decision; no artifact for the second reviewer | PR-time, reviewer-facing output. Same incumbent-absorption risk (X-212). |
| Critique (Google, internal) | Google engineers | Not purchasable | The reference standard users explicitly ask to be matched (S-408) | Not available externally; not built for agent-scale diffs | Users are asking for 'something in Critique's league' by name — that is a stated buying intent with no supplier in this block. |
| CRUX (Amazon, internal) | Amazon engineers | Not purchasable | Integrated with internal systems | Missing accept-and-merge and diff-based merging that peers have had for a decade (S-21) | Confirms even well-resourced orgs run on inadequate review tooling; not a market you can sell into. |
There is no verified paying competitor here, and that cuts both ways: no one has proven the willingness to pay either (X-214). The real competitors are free — GitHub's default view, an LLM chat window, and the 'lgtm' keystroke. Against those, the only defensible wedge is precision and ranking on diffs the incumbents currently render flat, and precision requires labelled defect data this block does not contain. Enter only if the concierge phase produces flags reviewers confirm as real at a high rate; otherwise you are shipping a second noise source into an already fatigued queue.
Pricing model
modelledA proposal, not an observation. Benchmarks come from the data; the ladder is ours.
Per-developer monthly seat on the GitHub org, billed to the engineering manager, with a floor. Seat-based because it is the shape the buyer already approves for Copilot/Cursor/Claude Code and needs no new budget category. Not per-PR: per-PR pricing punishes the exact behaviour (opening more agent PRs) you need to grow.
Team
$299/mo flat, up to 20 developers, 1 org
10–20 dev teams that have standardised on one coding agent
Growth
$20/developer/mo, 21+ developers
20–60 dev orgs with multiple repos and a named engineering manager
Design partner
$0 for 8 weeks, then Team price or…
First ten concierge accounts, in exchange for labelled real/not-real feedback on every flag
What the space charges
| No pricing benchmark available | n/a | Zero products with recorded pricing or revenue in this block (X-214). The $20/dev figure is anchored only on the fact that these teams already pay for per-seat AI coding tools (Copilot, Cursor,… |
Confidence in this pricing: low
Revenue scenarios
modelledArithmetic on the assumptions listed underneath. Change an assumption and the number changes.
| Case | Customers | ARPA / mo | MRR | ARR |
|---|---|---|---|---|
| base | 12 | $299 | $3,588 | $43,056 |
| upside | 40 | $420 | $16,800 | $201,600 |
| aggressive | 110 | $560 | $61,600 | $673,200 |
Assumptions behind these numbers
Disagree with one of these and the table above is wrong. That is the point of listing them.
- Horizon for all three cases: 12 months after a public launch, itself ~2 months after start (MVP time already assessed at 1–2 months).
- Base ARPA = the $299 Team tier, i.e. every base customer is a sub-20-dev team on the floor price. Assumption, not observed: no pricing evidence exists in this block.
- Base customer count of 12 assumes the founder personally closes design partners from HN and Product Hunt with no audience and no paid acquisition: ~200 targeted outreach contacts, ~15% take a call (30), ~40% install the GitHub App (12), and every installer that reaches 8 weeks converts.
- Upside assumes half the accounts are above 20 devs (blended $420 ARPA) and that Product Hunt plus one HN front-page post generate inbound installs at roughly 3x the outbound-only rate.
- Aggressive assumes a repeatable inbound channel and 110 paying orgs at a 28-dev blended average. It also assumes GitHub and the agent vendors have not shipped per-hunk risk flagging in that window — X-212 makes this the least reliable case, and it should be read as a ceiling, not a plan.
- Churn is excluded from all three cases. Given the S-18 mute-the-bot risk, treat 5%+ monthly logo churn as the realistic default and every ARR figure above as pre-churn gross.
- No enterprise/self-hosted revenue in any case, since self-hosted is explicitly deferred.
Market size
modelledReachable customers, not a top-down industry figure.
- Target customers
- Engineering orgs of roughly 10–60 developers that have already adopted a coding agent and are opening PRs above ~1k lines. No count of such orgs exists in this block; the only sizing evidence is 36 signals across HN (23), Product Hunt (12) and GitHub (1), which measures conversation, not population.
- Spend per year
- $3,588/yr at the Team floor; $5,000–$14,000/yr for a 20–60 dev org on the Growth tier. Both are arithmetic on the modelled prices above, not observed deals — no product in this block has recorded revenue (X-214).
- Reachability
- High for the first fifty, low thereafter. The affected people post in identifiable places (HN comment threads on agent code quality; Product Hunt launches in the same week) and install GitHub Apps without procurement. Beyond that, reaching engineering managers at 30+ dev orgs requires a channel this block gives no evidence for.
- Obtainable in 3 years
- Unquantifiable from this block, and saying so is the honest answer. If the base case holds and growth is linear on founder-led sales, ~$150–400k ARR in three years; the dominant variable is not market size but whether GitHub ships the feature (X-212), which would compress this to zero new logos.
- Comparable
- None available. The products list is empty and no revenue is recorded for any named tool, so there is no comparable to anchor against. Critique and CRUX are internal tools with no price.
Go to market
Named places, not channel categories. These signals came from somewhere.
First 10 customers
- Reply in the HN threads this cluster came from, with the hand-made artifact rather than a pitch: the 'CI for humans / enforcing that people understand each PR' thread (S-2979), the 'something in Critique's league' request (S-408), the 'current tools don't support this new coding paradigm' thread…
- Direct message the authors of S-219 ('told not to read PRs'), S-2980 (manager unable to prioritise refactor) and S-122 ('mentally dried after 5 PRs') offering a free hand-review of their next big PR.
- Ask HN: 'I hand-ranked the risky hunks in 20 agent-generated PRs — here is what was actually broken.' Post the taxonomy and the hit rate, including the misses.
- Search and monitor for the phrases these users actually type: 'reviewing AI generated PRs', 'large PR review tool', 'Critique alternative', 'agent PR too big to review', 'AI code review is not acceptable'.
- Product Hunt: launch in the same lane where S-2083, S-2866, S-2299 and S-2303 launched, but lead with the ranked-attention framing, not a risk score.
- Founders in the S-2108 situation (product built with Cursor/Lovable/Bolt/Replit, now stuck or insecure) reached through the same launch comment sections — low ACV, but fast paid validation of the audit output.
First 100
- Convert concierge notes into the GitHub App and publish it in the GitHub Marketplace, since these buyers install apps without procurement.
- Publish the flag taxonomy plus per-category confirmed-real rates as an open document, updated monthly; make the data the reason to be cited.
- One case study with a named team showing flagged-hunk defects that would have shipped, written for the engineering manager who needs refactor budget (S-2980).
- Integrate the check output into a weekly Slack digest so the manager, not just the reviewer, sees the product every week.
Scalable channels
- GitHub Marketplace listing (bottom-up install, no seller required)
- Ongoing HN presence built on published precision data rather than launches
- Product Hunt for burst attention only
What will not work
- The founder has no audience assumed; every first-ten channel above is manual outreach, which caps at ~30 conversations a month.
- HN is actively hostile to AI-reviews-AI positioning (S-18); a mistimed launch post burns the best channel once.
- Product Hunt launches in this exact lane are frequent (S-2083, S-2866, S-2299, S-2303, S-2527 within three weeks), so attention there is contested and short-lived.
- Bottom-up GitHub App installs by a curious developer do not produce a payer; the manager still has to be reached separately.
Roadmap
Each version ships something a user can use. No infrastructure-only phases.
- GitHub App with read access + webhook that emails the founder on PRs over 400 changed lines
- Hand-produced ranked-hunk note per PR, delivered within 3 working hours as one comment
- Fixed flag taxonomy written down and used consistently from PR one
- Per-flag real/not-real feedback collected from the reviewer by direct message
- 10 design-partner repos, zero code shipped to them
- Diff parsing + tree-sitter hunk-to-symbol mapping
- Deterministic detectors: secret-shaped literals, unresolvable symbol calls, swallowed error paths
- Single LLM pass constrained to rank and one-line reason, capped at 15 flags
- GitHub Check output with deep links and an explicit low-risk statement for the remainder
- In-check thumbs up/down per flag, stored as labels
- Repo-level tuning from dismissed-flag history
- Weekly Slack/email digest: high-risk PRs merged, flags dismissed, unreviewed merges
- Agent provenance labelling from commit trailers where available
- Published precision report per flag category
- Billing on the Team/Growth tiers, first paid conversions from design partners
Pivot paths
Where this goes if the first version does not land — and the number that says it did not.
Post-merge attribution: 'git blame for agents'
S-1908 states directly that when something breaks later, finding the causing change is harder than the fix. That is a narrower, more falsifiable job than pre-merge risk ranking and it is not something GitHub's diff view addresses. Thin evidence (one signal), so it is a pivot, not a start.
Manager-side AI-code risk reporting
S-2980 and S-346 describe a manager who cannot get review/refactor work prioritised for lack of visibility. Same data pipeline, different buyer, and reporting is harder for an agent vendor to absorb than a per-hunk score (X-212).
Fixed-fee audit and remediation of AI-built codebases
S-2108 shows founders already stuck with insecure, undeployable AI-built products, and S-44 shows credential leaks and broken integrations at team scale. Services validate willingness to pay within weeks and produce the labelled defect data the product needs; it does not scale, but nothing in this block proves a scalable version is purchasable.
Pivot trigger
If by day 60 fewer than 4 of the 10 concierge teams have opened the flagged hunks first (verifiable by asking the reviewer directly on each PR), or the confirmed-real rate across all flags is below 50%, stop building the linter and start the audit-services path within that week.
Risks and kill criteria
The thresholds at which the honest move is to stop. Written before you are attached to it.
Kill criteria
If one of these is true, stop. The value of writing them now is that you will not want to later.
- By day 30: if fewer than 12 of 20 interviewed reviewers can name a specific PR in the last 14 days they approved without properly reading, stop — the pain is narrated but not lived.
- By day 30: if fewer than 6 of 20 interviewees have tried to solve this themselves in the last month (pasting hunks into an LLM, writing a custom lint, splitting PRs by policy, blocking merges), stop — no self-help means no budget.
- By day 45: if fewer than 6 of the first 10 concierge notes get a reply confirming at least one flagged hunk was a real problem, stop.
- By day 60: if the confirmed-real rate across all delivered flags is under 50%, stop the linter (see pivot trigger).
- By day 90: if 0 of 10 design partners have agreed in writing to pay $299/mo after the free window, stop.
- At any point: if GitHub or a named agent vendor ships per-hunk risk scoring in the PR view, halt feature work within 2 weeks and re-evaluate against the attribution and manager-reporting pivots.
Validation plan
Seven days that cost nothing but time and can kill the idea before you build.
The next 7 days
- Day 1Re-read the 36 signals and extract every distinct commenter into a contact list with their thread link. Target 25 names from S-18, S-122, S-219, S-346, S-408, S-460, S-2303, S-2979, S-2980. No product, no landing page.
- Day 2Write the flag taxonomy by hand — 6 to 8 categories, drawn only from what users named (missing error handling, hardcoded secret/credential leak, hallucinated or unresolvable call, silent behaviour change, duplicated logic, broken test). One page. This is the thing you will test.
- Day 3Find 3 large public PRs on GitHub authored with an agent (commit trailers or obvious provenance) and hand-produce the ranked-hunk note for each. Time yourself. If one note takes over 3 hours, the concierge model does not hold and you need to know that now.
- Day 4Post the three notes publicly as a comment in the S-2979 'CI for humans' thread and the S-2303 large-PR thread, asking one question: 'is this the right ordering for your attention?' Record every reply verbatim.
- Day 5Send 25 individual messages offering a free hand-review of their next PR over 1k lines. Track reply rate and, specifically, how many name a recent incident caused by an unread merge.
- Day 6Run 5 calls of 20 minutes using the interview questions. Ask for the last big PR they approved and open it on the call; do not ask hypotheticals.
- Day 7Score against the day-30 kill criteria early: count self-help attempts, count named incidents, count teams who said yes to a free hand-review. Write the go/no-go in one paragraph and commit to it before writing any code.
Ask them this
Questions about what they did, not what they would do.
- Open the last PR over 1,000 lines you approved. How long did you actually spend in the diff, and which files did you not open?
- Have you approved a PR in the last two weeks without reading it? What made that the right call at the time?
- When something broke in production in the last quarter, how did you find the change that caused it, and how long did it take?
- What have you personally tried to make big agent PRs reviewable — splitting policy, custom lint, pasting into an LLM, refusing the PR? What happened to that attempt?
- If a check told you 'these 9 hunks out of 140 matter, skim the rest', what would make you distrust it enough to mute it?
- Who in your org would have to approve a $299/month tool that only produces PR comments, and what would they ask you for first?
- What do you already pay per developer for coding tools, and which line item would this come out of?
- Has your manager ever asked for evidence that code quality is degrading? What did you show them?
Sources and freshness
Every reference opens the original post. This is the part you should check first.
How sure are we, per claim
Where the data is thin, we say so instead of rounding up.
- demand
- Medium
- payment
- Low
- market size
- Low
- competitor gap
- Low
30 references from 36 signals · evaluation written Aug 13, 2026.
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Turn this into a spec
One Universal Core, then the exact file layout your platform expects — CLAUDE.md, .cursor/rules, a Lovable knowledge base, a Bolt prompt under its 400-word ceiling. Evidence travels with it.
Reading an open idea needs nothing. Generating a spec from it calls a model and costs real money, so it needs an account and credits — the cost is shown before you spend anything.