{"id":410,"date":"2026-09-04T18:46:26","date_gmt":"2026-09-04T18:46:26","guid":{"rendered":"https:\/\/texttoolz.com\/blog\/?p=410"},"modified":"2026-09-04T18:46:26","modified_gmt":"2026-09-04T18:46:26","slug":"sast-tools-false-positives","status":"publish","type":"post","link":"https:\/\/texttoolz.com\/blog\/sast-tools-false-positives\/","title":{"rendered":"Why Teams Replace Their Code Scanner Within Two Years"},"content":{"rendered":"

Two false-positive figures are published on the first page of results for this search. One says 71 to 88 percent. The other says 2.1 percent. They describe the same metric, they differ by more than an order of magnitude, and neither page states which kind of number it is publishing.<\/strong><\/p>\n

A second figure matters more than either of them. One vendor on this search reports that most teams replace their code scanner within two years. A category whose median purchase is discarded inside two years is not describing a detection problem.<\/p>\n

Nine of the ten results for this question are published by vendors in the category, and at least six rank themselves first. TextToolz sells no scanner, no application security platform and no security service, and takes no referral fee from anything named here.<\/p>\n

SAST, DAST and SCA, and why you need all three<\/h2>\n

ZeroPath publishes the clearest orientation table on this search, and it is worth carrying before any comparison, because buyers routinely shortlist products that do not solve the same problem.<\/p>\n

\n\n\n\n\n\n\n\n\n
What each scanning type covers. Structure credited to ZeroPath’s published comparison, read 17 August 2026.<\/caption>\n
<\/th>\nSAST<\/th>\nDAST<\/th>\nSCA<\/th>\n<\/tr>\n<\/thead>\n
What it scans<\/td>\nSource code or bytecode<\/td>\nThe running application<\/td>\nThird-party dependencies<\/td>\n<\/tr>\n
When it runs<\/td>\nDuring development<\/td>\nAfter deployment<\/td>\nDuring build or CI<\/td>\n<\/tr>\n
What it finds<\/td>\nCode-level flaws you wrote<\/td>\nRuntime issues<\/td>\nKnown CVEs in libraries<\/td>\n<\/tr>\n
Needs access to<\/td>\nSource code<\/td>\nAn application URL<\/td>\nPackage manifests<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

ZeroPath’s answer to whether you need all three is yes, and its reasoning is the useful part. SAST finds the vulnerabilities you<\/em> wrote: the injection in your query builder, the broken authorisation check. SCA finds the vulnerabilities somebody else<\/em> wrote: the known CVE in a package you installed. DAST finds what both miss, because it only appears when the application is actually running.<\/p>\n

Several products on this page combine SAST and SCA in one platform, which reduces tool sprawl and is the reason the two categories are usually shopped together.<\/p>\n

The two numbers that cannot both be right<\/h2>\n

This is the finding, and it takes about two minutes to verify for yourself by opening the two pages side by side. Both rank on the first page for this query, both are addressing the same prospective buyer, and both are describing the rate at which static analysis tools report problems that turn out not to be problems.<\/p>\n

\n\n\n\n\n\n\n
Published false-positive figures on this search, with the source each page attributes them to. Read 17 August 2026.<\/caption>\n
Source<\/th>\nClaim<\/th>\nAttributed by that page to<\/th>\n<\/tr>\n<\/thead>\n
pixee.ai<\/td>\nSAST false positive rates of 71 to 88 percent<\/strong><\/td>\nAggregated G2 reviewer data<\/td>\n<\/tr>\n
cycode.com<\/td>\nA 2.1 percent<\/strong> false positive rate, and 94 percent noise reduction<\/td>\nIts own OWASP benchmark results<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Both figures can be internally honest and the gap still leaves a buyer stranded, which is why this section exists.<\/p>\n

An aggregate of false positive rates across many tools, as experienced by reviewers using them on their own messy codebases, is a fundamentally different measurement from one tool’s score on a standardised benchmark suite that the vendor selected and ran. The first captures real conditions and unknown tuning. The second captures known conditions and optimal tuning.<\/p>\n

Neither page tells you which kind of number it is publishing, and a reader who encounters either in isolation will form a confident and opposite impression of the entire category.<\/p>\n

The OWASP benchmark itself is worth understanding here, because it is the mechanism behind the low figure. It is a public suite of deliberately vulnerable test cases with known answers, which makes it genuinely useful for comparing detection engines on a level field. What it is not is a measurement of your codebase. A vendor reporting its own score against a public benchmark is publishing a real number produced under conditions it controlled, and that is not the same thing as an independent evaluation.<\/p>\n

This page reports the contradiction rather than resolving it. Resolving it would require running the benchmark ourselves, and we did not.<\/p>\n

\n\"Bar
Two published false positive rates for the same metric, on incompatible populations.<\/figcaption><\/figure>\n

Detection is solved. Closing findings is not.<\/h2>\n

Pixee publishes the observation that reframes every comparison on this search, including its own category, and it deserves crediting for saying it about the whole field rather than only about competitors.<\/p>\n

Its claim: every SAST tool comparison online evaluates static analysis tools on language coverage, scan speed and pricing, and none of them asks whether the tool helps you actually close findings.<\/p>\n

Check that against the other nine results and it holds. Every one of them compares language coverage. Most compare scan speed. Several compare pricing models. Not one publishes a comparable figure for how many findings a team closed after adopting the tool, which is the only number that describes whether the purchase worked.<\/p>\n

Pixee supplies the cost of the gap as well: 30 to 50 percent of application security team time disappears into the triage queue. Findings arrive, most of them are not real, somebody has to work out which, and that somebody is the scarcest resource in the organisation.<\/p>\n

Then Endor Labs supplies the outcome metric that turns this from a complaint into evidence: most teams replace their scanner within two years.<\/strong><\/p>\n

Sit those two together. Teams buy a scanner, the scanner detects more, the backlog grows rather than shrinks, and within two years they replace it with a different scanner that also detects more. The purchase is being made against detection and judged against closure, which is why the same disappointment recurs.<\/p>\n

The practical consequence for anyone evaluating right now is a change of question. Not “how much does this find” but “how much of what this finds will my team actually close, and how long will each finding take to triage.”<\/p>\n

\n\"Four
What the category measures, and what it costs the teams buying it.<\/figcaption><\/figure>\n

What actually reduces noise<\/h2>\n

Two techniques on this search genuinely change the arithmetic rather than sorting it differently, and both are worth understanding before you compare feature lists.<\/p>\n

Reachability analysis<\/strong> is the first, and it applies to SCA. The traditional approach matches an installed dependency version against a database of known CVEs, which produces a finding for every vulnerable library present in your project. The problem is that presence is not exposure. Most projects import libraries whose vulnerable functions their own code never calls.<\/p>\n

Function-level reachability determines whether your code actually reaches the vulnerable function. That turns a list of several hundred findings into the handful that are genuinely exploitable in your application, which is a reduction in real work rather than a reordering of the same queue.<\/p>\n

The second approach accepts the noise and attacks the triage instead: an automated layer that assesses findings, discards the ones that are not real, and generates fixes as pull requests for the ones that are.<\/p>\n

Vendors publish figures for this. Merge rates, close rates, false-positive reduction percentages. Every one of them is a vendor claim about its own product, none has been independently replicated, and this page reports them as claims rather than as measurements throughout.<\/p>\n

The nine tools<\/h2>\n

Each entry states what the product is, its approach to noise, its pricing model where one is published, and one real limitation. What no entry states is accuracy, for the reason set out at the end.<\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n
The nine tools at a glance, read 17 August 2026.<\/caption>\n
Tool<\/th>\nCategory<\/th>\nApproach to noise<\/th>\n<\/tr>\n<\/thead>\n
Semgrep<\/td>\nSAST<\/td>\nCustom rules, speed<\/td>\n<\/tr>\n
Snyk<\/td>\nSAST and SCA<\/td>\nDeveloper workflow, database depth<\/td>\n<\/tr>\n
Checkmarx<\/td>\nFull platform<\/td>\nGovernance and correlation<\/td>\n<\/tr>\n
Endor Labs<\/td>\nSCA first<\/td>\nFunction-level reachability<\/td>\n<\/tr>\n
Pixee<\/td>\nTriage and remediation layer<\/td>\nSits on top of your scanner<\/td>\n<\/tr>\n
Cycode<\/td>\nPlatform<\/td>\nAI triage and remediation agents<\/td>\n<\/tr>\n
ZeroPath<\/td>\nSAST and SCA<\/td>\nAI analysis for logic flaws<\/td>\n<\/tr>\n
Orca Security<\/td>\nInfrastructure VM<\/td>\nExposure-based prioritisation<\/td>\n<\/tr>\n
Open-source stack<\/td>\nMixed<\/td>\nYou tune it yourself<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Semgrep<\/h3>\n

Semgrep appears in the top five of the one source on this search that claims independence, and it appears across the vendor lists too, consistently for the same reason: speed.<\/p>\n

ZeroPath, a direct competitor, credits Semgrep with sub-second scans while describing its own pull-request scans as completing in under two minutes, and characterises the difference as speed against thoroughness. A competitor conceding a rival’s advantage on a specific axis is more informative than most of what appears on this search.<\/p>\n

The architecture behind that speed is pattern matching against rules, which is the whole product decision. Rules are fast, transparent and inspectable. You can read a rule and understand exactly why a finding fired, which is not true of every tool on this page. You can also write your own, encoding your organisation’s specific mistakes rather than only the industry’s generic ones.<\/p>\n

That is a real advantage for a team with the appetite to own a rule set, and a real burden for a team without it. Rule-based analysis is exactly as good as its rules, and tuning is continuous work rather than a setup task. A team that buys Semgrep and never writes a rule has bought a generic scanner.<\/p>\n

The open-source community edition is why it appears on lists that otherwise contain only commercial products, and it makes evaluation nearly free.<\/p>\n

Best for teams with engineering capacity who will invest in their own rules and want to understand every finding. The limitation is that pattern matching finds the patterns you described, and business logic flaws frequently do not have a pattern.<\/p>\n

Snyk<\/h3>\n

Snyk appears on nearly every list on this search, which makes the most useful assessment of it the one written by a competitor being fair.<\/p>\n

Pixee describes Snyk as the best developer-first security platform, strongest for software composition analysis use cases, and adds a caveat worth quoting in substance: for SAST-heavy evaluations, compare language coverage and rule depth against dedicated tools. That is a competitor saying a rival is excellent at one half of its product and worth checking on the other.<\/p>\n

ZeroPath credits it with an industry-leading proprietary vulnerability database, and that points at the real differentiator in software composition analysis. The scanner is comparatively simple: match dependencies against known vulnerabilities. The database is the product, and the value is in how quickly and accurately it is maintained.<\/p>\n

The developer-first positioning is not marketing decoration either. Integration into the IDE means findings surface while the developer is still in the file that produced them, which is the cheapest possible moment to fix anything. A finding delivered three weeks later in a dashboard costs an order of magnitude more attention.<\/p>\n

Best for developer-centric teams that want SAST and SCA in one platform and value the workflow integration over depth in either half.<\/p>\n

The limitation is the one a competitor named: it is stronger on dependencies than on your own code, and a team whose primary risk is business logic should check that half carefully.<\/p>\n

Checkmarx<\/h3>\n

Checkmarx One is the enterprise all-in-one, combining SAST, SCA, DAST, IAST, API security, infrastructure-as-code scanning and container scanning under unified dashboards and compliance reporting, per ZeroPath’s description.<\/p>\n

It appears in the independent source’s top five and is named by Pixee alongside Fortify for the broadest language coverage available.<\/p>\n

The differentiator worth naming precisely is legacy language support. Checkmarx handles COBOL and ABAP, which most tools on this page do not touch. That fact is irrelevant to a startup and decisive for a bank, an insurer or a government department, where the systems holding the most sensitive data are frequently the oldest ones and the modern scanner simply cannot read them.<\/p>\n

Platform consolidation is the other argument and it cuts both ways honestly. One vendor across seven scanning types means one dashboard, one procurement, one integration effort and one correlation layer that can tell you the same weakness appears in three places. It also means the depth of each component is whatever the platform vendor decided, rather than what a specialist in that component would build.<\/p>\n

Best for large regulated enterprises with mixed and legacy codebases, and for organisations whose compliance reporting burden justifies the consolidation.<\/p>\n

The limitation is that a platform is bought as a platform, with the procurement cycle, implementation weight and multi-year commitment that implies. That is a particular risk given this page’s central finding: a two-year replacement cycle is survivable with a point tool and considerably more painful when seven scanning types were consolidated onto one vendor and the integration work has to be redone across all of them.<\/p>\n

Endor Labs<\/h3>\n

Endor Labs is reachability-first software composition analysis, and it is the source of this page’s most important sentence: most teams replace their scanner within two years.<\/p>\n

The technique is the product, so it is worth explaining properly. Conventional SCA reports every installed dependency carrying a known vulnerability, which is why an average project produces hundreds of findings on the first scan. Function-level reachability goes further and determines whether your application’s code path actually reaches the vulnerable function inside that dependency.<\/p>\n

The difference in practice is not a better sort order. It is the difference between a queue of four hundred findings and a queue of eleven, where the eleven are the ones an attacker could conceivably use. That is the only noise-reduction approach on this page that removes work rather than reorganising it.<\/p>\n

Two competitors place it accurately and both assessments are credited. Pixee calls it a complement to a SAST tool rather than a replacement. The independent source notes that its SAST capability is younger than its dependency engine, and that its natural home is the SCA category.<\/p>\n

Best for teams whose primary pain is dependency noise, which is most teams with a modern JavaScript or Python codebase.<\/p>\n

The limitation follows from the strength: it is an SCA-first product, so buying it addresses one half of the problem and you will still need something for the code your own team wrote.<\/p>\n

Pixee<\/h3>\n

Pixee is a triage and remediation layer that sits on top of a scanner you already own rather than replacing it, which makes it the clearest illustration of the shift this whole page describes.<\/p>\n

It also publishes the framing this article is built on, and credit is due for the fact that the criticism includes its own category: comparisons rank on coverage, speed and price, and none asks whether a tool helps you close findings.<\/p>\n

Its published figures, reported strictly as vendor claims with no independent replication: a 95 percent reduction in false positives at the triage stage, a 76 percent merge rate on automated fixes, and 70 to 95 percent false-positive reduction depending on which scanner it sits behind.<\/p>\n

Pricing is per repository and described as transparent, which is worth remarking on because it is nearly unique in this series. Across four categories examined this year, almost no enterprise security or infrastructure vendor publishes any price at all.<\/p>\n

The architectural argument is genuinely interesting for a buyer weighing a scanner replacement. If the backlog rather than the detection is the problem, replacing a scanner produces a new backlog, whereas a layer that closes findings applies to the scanner you already have and the sunk integration work survives.<\/p>\n

Best for teams that find thousands of issues and fix dozens, which is Pixee’s own description of its buyer and an unusually honest piece of targeting.<\/p>\n

The limitation is structural: it presumes you already own a scanner, so it is an addition to your stack rather than a consolidation of it.<\/p>\n

Cycode<\/h3>\n

Cycode is an AI-native platform spanning SAST, SCA and container security, with a risk intelligence graph offering code-to-cloud traceability and remediation agents that carry findings into the developer’s pull request with fixes attached.<\/p>\n

Its published claims, reported as claims throughout: a 2.1 percent false positive rate and 94 percent noise reduction against OWASP benchmark results, 31 percent faster scanning, and a 17-fold higher 90-day close rate for critical and high severity findings via its remediation agent.<\/p>\n

Code-to-cloud traceability is the substantive idea and it is worth separating from the marketing. Knowing that a vulnerability in a particular file ends up in a particular running container in a particular environment is what allows prioritisation by actual exposure rather than by severity label, and it is the same principle Orca applies to infrastructure.<\/p>\n

One observation about format, made factually. Cycode’s page includes a frequently-asked-questions block headed “Which SAST tool do application security pros rate as the best?”, and answers it with Cycode. That is the same self-ranking every vendor list on this search performs, expressed in a format that reads as a neutral answer to a general question. It is worth noticing rather than condemning, because six vendors here do the same thing less visibly.<\/p>\n

Best for teams wanting scanning, prioritisation and remediation from one platform.<\/p>\n

The limitation is that the strongest accuracy claim anywhere on this search is self-reported, and it is the number in direct contradiction with the other figure published on this page.<\/p>\n

ZeroPath<\/h3>\n

ZeroPath is AI-native SAST and SCA, and it ranks itself first in its own guide, as most of this list does.<\/p>\n

What separates it is the kind of evidence it publishes, which is more checkable than anything else on this search. Rather than a benchmark score, it lists named vulnerabilities its system found in major open-source projects: seven memory safety flaws in FFmpeg including heap buffer overflows and integer overflows, more than 170 valid bugs in curl, and CVE-2025-61928, an unauthenticated API key creation flaw in Better Auth permitting account takeover.<\/p>\n

The curl result is the interesting one for a technical buyer. That codebase already runs Coverity, CodeQL and OSS-Fuzz continuously, so finding valid bugs in it is a meaningfully harder test than a benchmark suite. Those CVEs are verifiable in principle through public records, and we audited none of them, which is stated.<\/p>\n

It also quotes an independent security researcher who tested AI SAST tools and rated it highly. That is a third-party assessment rather than a self-description, and it is a stronger form of evidence than a self-reported percentage, though it remains one person’s opinion.<\/p>\n

Its own page concedes a trade-off, which is unusual: pull-request scans complete in under two minutes, slower than Semgrep’s sub-second scans, in exchange for greater thoroughness.<\/p>\n

Best for teams chasing business logic vulnerabilities, which pattern-matching tools structurally struggle with, and for unusual language stacks including Rust and COBOL.<\/p>\n

The limitation is scan latency in a fast-moving pipeline, and that its strongest evidence is a set of discoveries rather than a comparative measurement.<\/p>\n

Orca Security<\/h3>\n

Orca belongs to the infrastructure half of vulnerability management rather than the code half, and it is included here specifically because buyers conflate the two and end up comparing a network scanner against a code analyser.<\/p>\n

It scans cloud workloads agentlessly, reading workloads, configurations, identities and data without installing anything, then scores every finding on a single context graph.<\/p>\n

The prioritisation principle it publishes is the transferable idea, and it is worth carrying even by readers who will never buy this product: an internet-facing workload with a path to sensitive data outranks an isolated development-box finding. Rank by real exposure rather than by CVE count or raw severity.<\/p>\n

That is the same insight as reachability analysis applied to infrastructure instead of dependencies. Both replace “is this vulnerable” with “can this actually be reached”, and both are the reason a queue shrinks rather than reorders.<\/p>\n

Orca also publishes a pricing model, subscription and quote-based, alongside a comparison table naming Tenable, Qualys, Rapid7, Wiz, Microsoft Defender, CrowdStrike, Intruder and Palo Alto Prisma Cloud with their deployment models and pricing structures. That table is unusually even-handed for a vendor page.<\/p>\n

Best for cloud-native and multi-cloud estates wanting coverage without agent deployment.<\/p>\n

The limitation is simply scope: it scans your infrastructure, not your source code, and it is not an alternative to anything else on this page. A team that buys it expecting to retire a SAST tool has misread the category, and a team that buys a SAST tool expecting cloud posture coverage has made the same mistake in the other direction.<\/p>\n

Open-source options<\/h3>\n

Orca names these honestly despite selling against them, which is worth crediting: OpenVAS for network and host scanning, Nuclei for template-based scanning, Trivy for containers and infrastructure as code, and DefectDojo for aggregating findings from multiple scanners. On the code side, Semgrep’s community edition and CodeQL belong in the same list.<\/p>\n

The capability is real. These are not toys, and several are used in production by organisations that could afford anything.<\/p>\n

Orca’s stated caveat is the correct one and applies to all of them: the limitation is operational rather than technical. You integrate, tune and maintain them yourself, with no vendor support and no unified prioritisation across domains. The tools are free and the engineering time is not.<\/p>\n

There is a connection to this page’s central finding that is worth drawing out. If most teams replace their commercial scanner within two years, part of what they are paying for is the ability to leave. A self-hosted stack has no vendor to fire, which means no recurring licence and also no escape hatch when the approach stops working. Whether that is an advantage depends entirely on whether you have the engineering capacity to change it yourself.<\/p>\n

Best for teams with genuine platform engineering capacity and a reason to keep scanning in-house.<\/p>\n

The limitation is that the total cost is real and simply appears on a different line of the budget. Engineering hours spent maintaining scanner infrastructure are hours not spent on the product, and unlike a licence fee that cost is invisible until somebody counts it.<\/p>\n

What none of this tells you<\/h2>\n

Accuracy, and the case for excluding it is stronger here than in any other category examined this year.<\/p>\n

Two figures published on this single search differ by more than an order of magnitude, both attributed to sources their publishers consider credible. We deployed no scanner, scanned no codebase and audited no benchmark result, so any accuracy ranking offered here would be invented.<\/p>\n

What a real evaluation requires is not complicated, and it is the only thing that will answer the question for you: your own repositories, in your own languages, with the findings triaged by your own team, and a count of what actually got merged rather than what got reported.<\/p>\n

Run that pilot against your current tool rather than in isolation, because the comparison that decides the purchase is not against a benchmark but against what you already have. Measure the number nobody publishes comparably. Not findings produced, but findings closed, and the hours spent per finding closed. A tool that surfaces four hundred issues of which your team fixes twelve is worse than one that surfaces thirty of which they fix twenty-five, and no comparison on this search would tell you that.<\/p>\n

\n\"Three
Three published figures, none of them measuring the same thing.<\/figcaption><\/figure>\n

How this comparison was built<\/h2>\n

Ten results were reviewed and eight extracted in full on 17 August 2026, with no extraction failures. Keyword research was run against Google’s autocomplete the same day, which publishes no search volume, and none is quoted here.<\/p>\n

The source-interest map is the context for everything above. Nine of the ten results are published by vendors in the category. At least six rank themselves first. One publishes a frequently-asked-questions block answering “which tool do professionals rate as the best” with its own name.<\/p>\n

Exactly one result claims independence in writing, stating that no vendor pays to appear in its ranking. That claim is the publisher’s own and we have not audited it, which is stated here rather than repeated as fact.<\/p>\n

No forum result ranks on this query at all, which is notable for a category with an unusually active practitioner community, and it means the place where engineers compare these tools candidly is not visible in these results.<\/p>\n

Every figure on this page is credited to the page that published it, with the source as that page described it. The two contradicting false-positive figures are reported as a contradiction and deliberately not resolved.<\/p>\n

Teams evaluating the DevSecOps end of this category may also want to look at GuardRails<\/a>, and the instruction from the section above applies to it as much as to anything else named here: ask what it closes, not what it finds.<\/p>\n

Frequently asked questions<\/h2>\n

These are the questions engineering and security teams search in this category, answered from the published sources named throughout.<\/p>\n

What is the difference between SAST, DAST and SCA?<\/h3>\n

SAST scans your source code during development and finds flaws you wrote. SCA scans third-party dependencies during build and finds known CVEs in libraries somebody else wrote. DAST scans the running application after deployment and finds runtime issues the other two cannot see.<\/p>\n

Do I need all three?<\/h3>\n

Yes, according to ZeroPath’s guide, because they catch categorically different things. A mature programme runs SAST and SCA at minimum, and several products combine those two in one platform to reduce tool sprawl.<\/p>\n

What is a typical SAST false positive rate?<\/h3>\n

The published figures contradict each other. One page states 71 to 88 percent from aggregated reviewer data; another claims 2.1 percent for itself from its own benchmark results. Neither states which kind of measurement it is, so treat both cautiously.<\/p>\n

What is reachability analysis?<\/h3>\n

Determining whether your code actually calls the vulnerable function inside a dependency, rather than only noting that a vulnerable version is installed. It is the one technique on this page that removes findings from the queue rather than reordering them.<\/p>\n

How long do teams keep a scanner?<\/h3>\n

Endor Labs reports that most teams replace their scanner within two years. That figure matters more than any detection claim, because it suggests the purchases are being made against detection and judged against closure.<\/p>\n

Which tool is the most accurate?<\/h3>\n

This page cannot tell you, and the contradicting figures above are why. Run a pilot on your own repositories, triage the findings with your own team, and count what gets merged rather than what gets reported.<\/p>\n","protected":false},"excerpt":{"rendered":"

Two false-positive figures are published on the first page of results for this search. One says 71 to 88 percent. The other says 2.1 percent. They describe the same metric, they differ by more than an order of magnitude, and neither page states which kind of number it is publishing. A second figure matters more […]<\/p>\n","protected":false},"author":1,"featured_media":422,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-410","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"_links":{"self":[{"href":"https:\/\/texttoolz.com\/blog\/wp-json\/wp\/v2\/posts\/410","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/texttoolz.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/texttoolz.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/texttoolz.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/texttoolz.com\/blog\/wp-json\/wp\/v2\/comments?post=410"}],"version-history":[{"count":3,"href":"https:\/\/texttoolz.com\/blog\/wp-json\/wp\/v2\/posts\/410\/revisions"}],"predecessor-version":[{"id":658,"href":"https:\/\/texttoolz.com\/blog\/wp-json\/wp\/v2\/posts\/410\/revisions\/658"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/texttoolz.com\/blog\/wp-json\/wp\/v2\/media\/422"}],"wp:attachment":[{"href":"https:\/\/texttoolz.com\/blog\/wp-json\/wp\/v2\/media?parent=410"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/texttoolz.com\/blog\/wp-json\/wp\/v2\/categories?post=410"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/texttoolz.com\/blog\/wp-json\/wp\/v2\/tags?post=410"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}